Evolving connectionist systems - the knowledge engineering approach (2. ed.)
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[1] Jacek M. Zurada,et al. Pruning via Dynamic Adaptation of the Forgetting Rate in Structural Learning , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).
[2] Christopher G. Atkeson,et al. Constructive Incremental Learning from Only Local Information , 1998, Neural Computation.
[3] L. Medsker,et al. Design and development of hybrid neural network and expert systems , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).
[4] G. Lewicki,et al. Approximation by Superpositions of a Sigmoidal Function , 2003 .
[5] Samuel Russell Hampden Joseph,et al. Theories of adaptive neural growth , 1998 .
[6] Nicolaos B. Karayiannis,et al. Growing radial basis neural networks: merging supervised and unsupervised learning with network growth techniques , 1997, IEEE Trans. Neural Networks.
[7] Nikola Kasabov,et al. Neuro-Fuzzy Techniques for Intelligent Information Systems , 1999 .
[8] Ben-Qiong Hu,et al. Quantum Pattern Recognition of Classical Signal , 2007 .
[9] J. Mendel. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .
[10] J. G. Taylor,et al. From Wetware to Hardware: Reverse Engineering Using Probabilistic RAMs , 1992 .
[11] Nikola K. Kasabov,et al. Gene Regulatory Network Discovery from Time-Series Gene Expression Data - A Computational Intelligence Approach , 2004, ICONIP.
[12] S. Grossberg,et al. ART 2: self-organization of stable category recognition codes for analog input patterns. , 1987, Applied optics.
[13] Michio Sugeno,et al. Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.
[14] Phil Husbands,et al. Evolutionary robotics , 2014, Evolutionary Intelligence.
[15] Ken-ichi Funahashi,et al. On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.
[16] G. W. Hatfield,et al. DNA microarrays and gene expression , 2002 .
[17] Shaoning Pang,et al. Incremental linear discriminant analysis for classification of data streams , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[18] P G Baker,et al. Recent developments in biological sequence databases. , 1998, Current opinion in biotechnology.
[19] James C. Bezdek,et al. A Review of Probabilistic, Fuzzy, and Neural Models for Pattern Recognition , 1996, J. Intell. Fuzzy Syst..
[20] Nikola K. Kasabov,et al. HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems , 1999, Neural Networks.
[21] Joel White,et al. Odor recognition in an artificial nose by spatio-temporal processing using an olfactory neuronal network , 1999, Neurocomputing.
[22] David Zhang,et al. An adaptive model of person identification combining speech and image information , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..
[23] David G. Stork. Sources of Neural Structure in Speech and Language Processing , 1991, Int. J. Neural Syst..
[24] Madan M. Gupta. Fuzzy logic and neural networks , 1992, [Proceedings 1992] IEEE International Conference on Systems Engineering.
[25] Michael A. Gibson,et al. Modeling the Activity of Single Genes , 1999 .
[26] Nikola Kasabov,et al. Evolving connectionist systems , 2002 .
[27] C. L. Giles,et al. Constructing deterministic finite-state automata in sparse recurrent neural networks , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).
[28] J. J. Hopfield,et al. ‘Unlearning’ has a stabilizing effect in collective memories , 1983, Nature.
[29] Volker Tresp,et al. Network Structuring and Training Using Rule-Based Knowledge , 1992, NIPS.
[30] Eric O. Postma,et al. AVIS: a connectionist-based framework for integrated auditory and visual information processing , 2000, Inf. Sci..
[31] T. Poggio,et al. Multiclass cancer diagnosis using tumor gene expression signatures , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[32] James A. Hendler,et al. Integrating Neural Network and Expert Reasoning: An Example , 1991 .
[33] Shigeo Abe,et al. A method for fuzzy rules extraction directly from numerical data and its application to pattern classification , 1995, IEEE Trans. Fuzzy Syst..
[34] Shaoning Pang,et al. An Incremental Principal Component Analysis for Chunk Data , 2006, 2006 IEEE International Conference on Fuzzy Systems.
[35] C. A. Ferguson,et al. Talking to Children , 1977 .
[36] Tony R. Martinez,et al. Quantum associative memory , 2000, Inf. Sci..
[37] P. Benioff. The computer as a physical system: A microscopic quantum mechanical Hamiltonian model of computers as represented by Turing machines , 1980 .
[38] Ganesh K. Venayagamoorthy,et al. Function Approximations with Multilayer Perceptrons and Simultaneous Recurrent Metworks , 2004 .
[39] Kumar S. Ray,et al. Neuro Fuzzy Approach to Pattern Recognition , 1997, Neural Networks.
[40] Michael A. Arbib,et al. The metaphorical brain : an introduction to cybernetics as artificial intelligence and brain theory , 1972 .
[41] Michael Ruogu Zhang,et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.
[42] Michel Toulouse,et al. Automatic Quantum Computer Programming: A Genetic Programming Approach , 2006, Genetic Programming and Evolvable Machines.
[43] Sukumar Chakraborty,et al. A neuro-fuzzy framework for inferencing , 2002, Neural Networks.
[44] Stefan Wermter,et al. A Hybrid Symbolic/Connectionist Model for Noun Phrase Understanding , 1989 .
[45] Brian R Glasberg,et al. Derivation of auditory filter shapes from notched-noise data , 1990, Hearing Research.
[46] Abraham Kandel,et al. Neuro-Fuzzy Pattern Recognition , 2000 .
[47] Michael R. Green,et al. Gene Expression , 1993, Progress in Gene Expression.
[48] H. Robinson,et al. Determining the activation time course of synaptic AMPA receptors from openings of colocalized NMDA receptors. , 1999, Biophysical journal.
[49] Shigeo Abe,et al. An Incremental Learning Algorithm of Ensemble Classifier Systems , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[50] John A. Barnden,et al. Encoding techniques for complex information structures in connectionist systems , 1991 .
[51] Takeshi Aihara,et al. Hippocampal LTP Depends on Spatial and Temporal Correlation of Inputs , 1996, Neural Networks.
[52] Noam Chomsky,et al. The Minimalist Program , 1992 .
[53] Nikola K. Kasabov,et al. On-line pattern analysis by evolving self-organizing maps , 2003, Neurocomputing.
[54] Nikola Kasabov,et al. Computational neurogenetic modeling: integration of spiking neural networks, gene networks, and signal processing techniques , 2004 .
[55] Susan P. Worner,et al. Dynamic Neuro-fuzzy Inference and Statistical Models for Risk Analysis of Pest Insect Establishment , 2004, ICONIP.
[56] Stephen Grossberg,et al. ART 3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures , 1990, Neural Networks.
[57] Kevin Bluff,et al. Genetic optimisation of control parameters of a neural network , 1995, Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.
[58] M. Borodovsky,et al. GeneMark.hmm: new solutions for gene finding. , 1998, Nucleic acids research.
[59] Teuvo Kohonen,et al. Self-Organizing Maps, Second Edition , 1997, Springer Series in Information Sciences.
[60] Nobuyuki Matsui,et al. Qubit neural network and its learning efficiency , 2005, Neural Computing & Applications.
[61] Nikola K. Kasabov,et al. The Application of Hybrid Evolving Connectionist Systems to Image Classification , 2000, J. Adv. Comput. Intell. Intell. Informatics.
[62] Bartlett W. Mel. SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition , 1997, Neural Computation.
[63] Simei Gomes Wysoski,et al. Computational Neurogenetic Modeling: A Methodology to Study Gene Interactions Underlying Neural Oscillations , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[64] Andreas D. Baxevanis,et al. The Molecular Biology Database Collection: an online compilation of relevant database resources , 2000, Nucleic Acids Res..
[65] Shaoning Pang,et al. Incremental learning for online face recognition , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[66] Nikola K. Kasabov,et al. Global, local and personalised modeling and pattern discovery in bioinformatics: An integrated approach , 2007, Pattern Recognit. Lett..
[67] Simei Gomes Wysoski,et al. On-Line Learning with Structural Adaptation in a Network of Spiking Neurons for Visual Pattern Recognition , 2006, ICANN.
[68] Wolfgang Maass,et al. Computing with spiking neurons , 1999 .
[69] Mark S. Boguski,et al. Bioinformatics–a new era , 1998 .
[70] Joy Hirsch,et al. Distinct cortical areas associated with native and second languages , 1997, Nature.
[71] Douglas H. Fisher,et al. Knowledge Acquisition Via Incremental Conceptual Clustering , 1987, Machine Learning.
[72] Martin Anthony,et al. Computational learning theory: an introduction , 1992 .
[73] Michael C. Mozer,et al. Learning explicit rules in a neural network , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.
[74] John Moody,et al. Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.
[75] Nikola Kasabov,et al. Evolutionary computation for dynamic parameter optimisation of evolving connectionist systems for on-line prediction of time series with changing dynamics , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..
[76] D. D. Greenwood. A cochlear frequency-position function for several species--29 years later. , 1990, The Journal of the Acoustical Society of America.
[77] Nikola Kasabov,et al. Estimating risk of events using SOM models: A case study on invasive species establishment , 2006 .
[78] Teresa Bernarda Ludermir,et al. Evolutionary strategies and genetic algorithms for dynamic parameter optimization of evolving fuzzy neural networks , 2005, 2005 IEEE Congress on Evolutionary Computation.
[79] A. A. Mullin,et al. Principles of neurodynamics , 1962 .
[80] Richard J. Duro,et al. Evolutionary generation and training of recurrent artificial neural networks , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.
[81] David Saad,et al. Online Learning in Radial Basis Function Networks , 1997, Neural Computation.
[82] Liang Goh,et al. An Integrated Feature Selection and Classification Method to Select Minimum Number of Variables on the Case Study of Gene Expression Data , 2005, J. Bioinform. Comput. Biol..
[83] N. Kasabov,et al. Linear and non-linear pattern recognition models for classification of fruit from visible–near infrared spectra , 2000 .
[84] T. Sejnowski,et al. Irresistible environment meets immovable neurons , 1997, Behavioral and Brain Sciences.
[85] Lokendra Shastri,et al. A Biological Grounding of Recruitment Learning and Vicinal Algorithms , 1999 .
[86] A. Gray,et al. I. THE ORIGIN OF SPECIES BY MEANS OF NATURAL SELECTION , 1963 .
[87] R. Brown,et al. Smoothing, Forecasting, and Prediction of Discrete Time Series , 1965 .
[88] Nikola K. Kasabov,et al. Integrating regression formulas and kernel functions into locally adaptive knowledge-based neural networks: A case study on renal function evaluation , 2006, Artif. Intell. Medicine.
[89] Todd,et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning , 2002, Nature Medicine.
[90] Michael J. Watts,et al. Adaptive speech recognition with evolving connectionist systems , 2003, Inf. Sci..
[91] James C. Bezdek,et al. Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.
[92] Thomas Martinetz,et al. 'Neural-gas' network for vector quantization and its application to time-series prediction , 1993, IEEE Trans. Neural Networks.
[93] Peter W. Shor,et al. Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer , 1995, SIAM Rev..
[94] Yves Chauvin,et al. A Back-Propagation Algorithm with Optimal Use of Hidden Units , 1988, NIPS.
[95] Yue Shi,et al. A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).
[96] Dimitar Filev,et al. On-Line Evolution of Takagi-Sugeno Fuzzy Models , 2004, IFAC Proceedings Volumes.
[97] Friedrich Ungerer,et al. An introduction to cognitive linguistics , 1999 .
[98] Patrik D'haeseleer,et al. Genetic network inference: from co-expression clustering to reverse engineering , 2000, Bioinform..
[99] Andreas Ziehe,et al. Adaptive On-line Learning in Changing Environments , 1996, NIPS.
[100] Nikola Kasabov. Evolving Connectionist-based Decision Support Systems , 2003 .
[101] Nikola Kasabov,et al. Artificial Immune Networks as a Paradigm for Classification and Profiling of Gene Expression Data , 2005 .
[102] Nikola K. Kasabov,et al. An efficient greedy K-means algorithm for global gene trajectory clustering , 2006, Expert Syst. Appl..
[103] John G. Taylor,et al. Neural networks for consciousness , 1997, Neural Networks.
[104] James Gleick,et al. Chaos, Making a New Science , 1987 .
[105] Nikola Kasabov,et al. Fuzzy clustering of gene expression data , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).
[106] Mick F. Tuite,et al. Post-Transcriptional Control of Gene Expression , 1990, NATO ASI Series.
[107] J. J. Hopfield,et al. “Neural” computation of decisions in optimization problems , 1985, Biological Cybernetics.
[108] H. R. Berenji,et al. Fuzzy Logic Controllers , 1992 .
[109] John H. Andreae,et al. The chaotic self-organizing map , 1993, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.
[110] Gerald Sommer,et al. On-line Learning with Dynamic Cell Structures , 2004 .
[111] Nikola Kasabov,et al. Biologically Plausible Computational Neurogenetic Models: Modeling the Interaction Between Genes, Neurons and Neural Networks , 2005 .
[112] Jyh-Shing Roger Jang,et al. ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..
[113] Jude W. Shavlik,et al. Knowledge-Based Artificial Neural Networks , 1994, Artif. Intell..
[114] Lipo Wang,et al. Data Mining With Computational Intelligence , 2006, IEEE Transactions on Neural Networks.
[115] M. Arbib. Brains, Machines, and Mathematics , 1987, Springer US.
[116] Susan P. Worner,et al. Ecoclimatic assessment of potential establishment of exotic pests. , 1988 .
[117] A M Liberman,et al. Perception of the speech code. , 1967, Psychological review.
[118] George J. Klir,et al. Conceptual Foundations Of Quantum Mechanics: The Role Of Evidence Theory, Quantum Sets, And Modal Logic , 1999 .
[119] Philippe Gaussier,et al. A topological neural map for on-line learning: emergence of obstacle avoidance in a mobile robot , 1994 .
[120] Catherine L. Harris,et al. Connectionism and Cognitive Linguistics , 1990 .
[121] Shaoning Pang,et al. One-Pass Incremental Membership Authentication by Face Classification , 2004, ICBA.
[122] Tin Wee Tan,et al. Information Processing and Living Systems , 2005 .
[123] Terrence J. Sejnowski,et al. The Computational Brain , 1996, Artif. Intell..
[124] Robert A. Jacobs,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.
[125] Ronald W. Davis,et al. A genome-wide transcriptional analysis of the mitotic cell cycle. , 1998, Molecular cell.
[126] Sushmita Mitra,et al. Neuro-fuzzy rule generation: survey in soft computing framework , 2000, IEEE Trans. Neural Networks Learn. Syst..
[127] Ajit Narayanan,et al. Quantum artificial neural network architectures and components , 2000, Inf. Sci..
[128] Enrico Blanzieri,et al. Learning Radial Basis Function Networks On-line , 1996, International Conference on Machine Learning.
[129] Mark S. Nixon,et al. Generating-shrinking algorithm for learning arbitrary classification , 1994, Neural Networks.
[130] Wlodzislaw Duch,et al. Extraction of Logical Rules from Neural Networks , 1998, Neural Processing Letters.
[131] R. Miesfeld,et al. Applied Molecular Genetics , 1999 .
[132] Stephen M. Mount,et al. A catalogue of splice junction sequences. , 1982, Nucleic acids research.
[133] B McNulty,et al. To compute...or not to compute? , 1988, Ontario dentist.
[134] Shaoning Pang,et al. Two-Class SVM Trees (2-SVMT) for Biomarker Data Analysis , 2006, ISNN.
[135] Nikola Kasabov,et al. Computational Neurogenetic Modeling , 2007 .
[136] C. A. Murthy,et al. A modified metric to compute distance , 1992, Pattern Recognit..
[137] S. P. Lloyd,et al. Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.
[138] James R. Koehler,et al. Statistics in Engineering: A Practical Approach , 1996 .
[139] James L. McClelland,et al. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. , 1995, Psychological review.
[140] Marek A. Perkowski,et al. Multiple-Valued Quantum Circuits and Research Challenges for Logic Design and Computational Intelligence Communities , 2022 .
[141] Nikola K. Kasabov,et al. Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning , 2001, IEEE Trans. Syst. Man Cybern. Part B.
[142] M. West,et al. Bayesian forecasting and dynamic models , 1989 .
[143] Andy Clark,et al. Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing , 1989 .
[144] Vera Kurková,et al. Kolmogorov's theorem and multilayer neural networks , 1992, Neural Networks.
[145] Dimitar Filev,et al. Generation of Fuzzy Rules by Mountain Clustering , 1994, J. Intell. Fuzzy Syst..
[146] R.J. Machado,et al. Evolutive fuzzy neural networks , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.
[147] Paul M. Frank,et al. Identification of fuzzy relational models for fault detection , 1999 .
[148] S. J. Sinclair,et al. The development of the Otago speech database , 1995, Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.
[149] Christopher L. Scofield,et al. Neural networks and speech processing , 1991, The Kluwer international series in engineering and computer science.
[150] Nikola Kasabov,et al. Discovering gene regulatory networks from gene expression data with the use of evolving connectionist systems , 2004 .
[151] G. Church,et al. Systematic determination of genetic network architecture , 1999, Nature Genetics.
[152] J. M. Williams,et al. Correlations between immediate early gene induction and the persistence of long-term potentiation , 1993, Neuroscience.
[153] Bernard Widrow,et al. Adaptive switching circuits , 1988 .
[154] J. Collado-Vides. Integrative Approaches to Molecular Biology , 1996 .
[155] Russell C. Eberhart,et al. A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.
[156] Yoshiki Uchikawa,et al. An efficient finding of fuzzy rules using a new approach to genetic based machine learning , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..
[157] S. Pinker. The language instinct : how the mind creates language , 1995 .
[158] C. Koch,et al. Quantum mechanics in the brain , 2006, Nature.
[159] G. RESCONP,et al. A DATA MODEL FOR THE MORPHOGENETIC NEURON , 2000 .
[160] C. Smith,et al. Adaptive Coding of Monochrome and Color Images , 1977, IEEE Trans. Commun..
[161] Joseph Picone,et al. Signal modeling techniques in speech recognition , 1993, Proc. IEEE.
[162] Nikola Kasabov,et al. Modelling the Emergence of Speech and Language Through Evolving Connectionist Systems , 2000 .
[163] Nikola K. Kasabov,et al. A Hybrid Genetic Algorithm and Expectation Maximization Method for Global Gene Trajectory Clustering , 2005, J. Bioinform. Comput. Biol..
[164] Ralph R. Martin,et al. Incremental Eigenanalysis for Classification , 1998, BMVC.
[165] Nikola Kasabov,et al. Evolving computational intelligence systems , 2005 .
[166] S. Grossberg,et al. The Hippocampus and Cerebellum in Adaptively Timed Learning, Recognition, and Movement , 1996, Journal of Cognitive Neuroscience.
[167] Teresa Bernarda Ludermir,et al. EFuNNs Ensembles Construction Using a Clustering Method and a Coevolutionary Genetic Algorithm , 2006, 2006 IEEE International Conference on Evolutionary Computation.
[168] Masumi Ishikawa,et al. Structural learning with forgetting , 1996, Neural Networks.
[169] Gerald Sommer,et al. Dynamic Cell Structure Learns Perfectly Topology Preserving Map , 1995, Neural Computation.
[170] Andrew W. Moore,et al. Acquisition of Dynamic Control Knowledge for a Robotic Manipulator , 1990, ML.
[171] Xin Yao,et al. Evolutionary Artificial Neural Networks , 1993, Int. J. Neural Syst..
[172] L. Wolpert. Artwork CD-ROM for Principles of development , 1998 .
[173] Xiaowei Zhou,et al. Real-time joint Landmark Recognition and Classifier Generation by an Evolving Fuzzy System , 2006, 2006 IEEE International Conference on Fuzzy Systems.
[174] Michael J. Watts,et al. Nominal-scale Evolving Connectionist Systems , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[175] Marvin Minsky,et al. Perceptrons: An Introduction to Computational Geometry , 1969 .
[176] Michael Arbib,et al. From Vision to Action via Distributed Computation , 1997 .
[177] Siming Liu,et al. Dynamic topology representing networks , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).
[178] Kishan G. Mehrotra,et al. Efficient classification for multiclass problems using modular neural networks , 1995, IEEE Trans. Neural Networks.
[179] David E. Rumelhart,et al. Predicting the Future: a Connectionist Approach , 1990, Int. J. Neural Syst..
[180] László T. Kóczy,et al. Fuzzy systems and approximation , 1997, Fuzzy Sets Syst..
[181] Nikola Kasabov,et al. Efficient global clustering using the Greedy Elimination Method , 2004 .
[182] Les E. Atlas,et al. The challenge of spoken language systems: research directions for the nineties , 1995, IEEE Trans. Speech Audio Process..
[183] A. Wolf,et al. Determining Lyapunov exponents from a time series , 1985 .
[184] James V. Candy,et al. Adaptive and Learning Systems for Signal Processing, Communications, and Control , 2006 .
[185] Jacques Gautrais,et al. SpikeNET: A simulator for modeling large networks of integrate and fire neurons , 1999, Neurocomputing.
[186] Nikola Kasabov,et al. A two-stage methodology for gene regulatory network extraction from time-course gene expression data , 2004 .
[187] Koichiro Yamauchi,et al. Sleep Learning - An Incremental Learning System Inspired by Sleep Behavior- , 2006, 2006 IEEE International Conference on Fuzzy Systems.
[188] Shaoning Pang,et al. Image and Fractal Information Processing for Large-Scale Chemoinformatics, Genomics Analyses and Pattern Discovery , 2006, PRIB.
[189] Daming Shi,et al. ESOFCMAC: Evolving Self-Organizing Fuzzy Cerebellar Model Articulation Controller , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[190] I. Dimopoulos,et al. Application of neural networks to modelling nonlinear relationships in ecology , 1996 .
[191] Farmer,et al. Predicting chaotic time series. , 1987, Physical review letters.
[192] Stephen L. Chiu,et al. Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..
[193] James C. Bezdek,et al. On cluster validity for the fuzzy c-means model , 1995, IEEE Trans. Fuzzy Syst..
[194] Boris Bacic. Towards a neuro fuzzy tennis coach: automated extraction of the region of interest (ROI) , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).
[195] H. Carter. Fuzzy Sets and Systems — Theory and Applications , 1982 .
[196] Yukio Kosugi,et al. An oscillation-driven neural network for the simulation of an olfactory system , 2003, Neural Computing & Applications.
[197] Shigeru Tanaka,et al. Topology of Visual Cortical Maps , 1997 .
[198] A. Konnerth,et al. Long-term potentiation and functional synapse induction in developing hippocampus , 1996, Nature.
[199] S. Segalowitz. Language functions and brain organization , 1983 .
[200] J. McCauley. Chaos, dynamics, and fractals : an algorithmic approach to deterministic chaos , 1993 .
[201] Nikola K. Kasabov,et al. Fast neural network ensemble learning via negative-correlation data correction , 2005, IEEE Transactions on Neural Networks.
[202] Madan M. Gupta,et al. On the principles of fuzzy neural networks , 1994 .
[203] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[204] Zbigniew Michalewicz,et al. Evolutionary Algorithms in Engineering Applications , 1997, Springer Berlin Heidelberg.
[205] Michio Sugeno,et al. An introductory survey of fuzzy control , 1985, Inf. Sci..
[206] Irena Koprinska,et al. Video segmentation of MPEG compressed data , 1998, 1998 IEEE International Conference on Electronics, Circuits and Systems. Surfing the Waves of Science and Technology (Cat. No.98EX196).
[207] Nikola Kasabov,et al. Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines , 2002, IEEE Transactions on Neural Networks.
[208] Nikola K. Kasabov,et al. Adaptation and interaction in dynamical systems: Modelling and rule discovery through evolving connectionist systems , 2006, Appl. Soft Comput..
[209] Michael C. Mozer,et al. Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment , 1988, NIPS.
[210] Wilfrid S. Kendall,et al. Networks and Chaos - Statistical and Probabilistic Aspects , 1993 .
[211] Jude Shavlik,et al. Refinement ofApproximate Domain Theories by Knowledge-Based Neural Networks , 1990, AAAI.
[212] Zbigniew Michalewicz,et al. Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.
[213] P. Jusczyk. The discovery of spoken language , 1997 .
[214] Nikola Kasabov,et al. Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief] , 1996, IEEE Transactions on Neural Networks.
[215] Arnaud Delorme,et al. Networks of integrate-and-fire neurons using Rank Order Coding B: Spike timing dependent plasticity and emergence of orientation selectivity , 2001, Neurocomputing.
[216] H. M. Wain. Introduction to Bioinformatics. Cell and Molecular Biology in Action Series. By T. K. Attwood and D. J. Parry‐Smith (Series Editor: E. Wood). Harlow, Essex: Addison Wesley Longman. 1999. Pp. 218. £17.99 (paperback). , 1999 .
[217] John J. Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities , 1999 .
[218] N. Kasabov,et al. Gene trajectory clustering with a hybrid genetic algorithm and expectation maximization method , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[219] W. Freeman. Simulation of chaotic EEG patterns with a dynamic model of the olfactory system , 1987, Biological Cybernetics.
[220] Stephen Grossberg,et al. Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.
[221] CHEE PENG LIM,et al. An Incremental Adaptive Network for On-line Supervised Learning and Probability Estimation , 1997, Neural Networks.
[222] David Zhang,et al. An Evolving Neural Network Model for Person Verification Combining Speech and Image , 2004, ICONIP.
[223] Vojislav Kecman. Support Vector Machines , 2001 .
[224] Edward E. Smith,et al. Categories and concepts , 1984 .
[225] James J. Buckley,et al. Are regular fuzzy neural nets universal approximators? , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).
[226] S. Manel,et al. Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird , 1999 .
[227] Michael J. Watts,et al. FuNN/2 - A Fuzzy Neural Network Architecture for Adaptive Learning and Knowledge Acquisition , 1997, Inf. Sci..
[228] A. Ralescu,et al. Recognition of and reasoning about facial expressions using fuzzy logic , 1993, Proceedings of 1993 2nd IEEE International Workshop on Robot and Human Communication.
[229] E. de Boer,et al. On cochlear encoding: potentialities and limitations of the reverse-correlation technique. , 1978, The Journal of the Acoustical Society of America.
[230] James C. Bezdek,et al. Analysis of fuzzy information , 1987 .
[231] Nikola Kasabov,et al. Computational Intelligence, Bioinformatics and Computational Biology: A Brief Overview of Methods, Problems and Perspectives , 2005 .
[232] Wulfram Gerstner,et al. Spiking Neuron Models , 2002 .
[233] D.P. Filev,et al. An approach to online identification of Takagi-Sugeno fuzzy models , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[234] P J Webros. BACKPROPAGATION THROUGH TIME: WHAT IT DOES AND HOW TO DO IT , 1990 .
[235] Nikola Kasabov,et al. Knowledge-based neural networks for gene expression data analysis, modelling and profile discovery , 2004 .
[236] Jude Shavlik,et al. An Approach to Combining Explanation-based and Neural Learning Algorithms , 1989 .
[237] M. Ringnér,et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.
[238] P. Brown,et al. Exploring the metabolic and genetic control of gene expression on a genomic scale. , 1997, Science.
[239] Nikola K. Kasabov,et al. Modeling the emergence of bilingual acoustic clusters: a preliminary case study , 2003, Inf. Sci..
[240] Nikola K. Kasabov,et al. Transductive Knowledge Based Fuzzy Inference System for Personalized Modeling , 2005, FSKD.
[241] Geoffrey E. Hinton,et al. A Distributed Connectionist Production System , 1988, Cogn. Sci..
[242] F. Crick. Central Dogma of Molecular Biology , 1970, Nature.
[243] Biing-Hwang Juang,et al. Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.
[244] Nikola Kasabov,et al. A Methodology and a System for Adaptive Speech Recognition in a Noisy Environment Based on Adaptive Noise Cancellation and Evolv- ing Fuzzy Neural Networks , 2000 .
[245] Hiroshi Okamoto,et al. Temporal Event Association and Output-Dependent Learning: A Proposed Scheme of Neural Molecular Connections , 1999, J. Adv. Comput. Intell. Intell. Informatics.
[246] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[247] G. Altmann. Cognitive models of speech processing , 1991 .
[248] Petra Perner,et al. Data Mining - Concepts and Techniques , 2002, Künstliche Intell..
[249] E. Bienenstock,et al. Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[250] Jude W. Shavlik,et al. Extracting Refined Rules from Knowledge-Based Neural Networks , 1993, Machine Learning.
[251] T. Martin McGinnity,et al. A Supervised STDP Based Training Algorithm with Dynamic Threshold Neurons , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[252] Daniel J. Amit,et al. Modeling brain function: the world of attractor neural networks, 1st Edition , 1989 .
[253] D. Contreras,et al. Spatiotemporal Analysis of Local Field Potentials and Unit Discharges in Cat Cerebral Cortex during Natural Wake and Sleep States , 1999, The Journal of Neuroscience.
[254] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[255] S. Renals,et al. Phoneme classification experiments using radial basis functions , 1989, International 1989 Joint Conference on Neural Networks.
[256] James Kennedy,et al. Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.
[257] Fu,et al. Integration of neural heuristics into knowledge-based inference , 1989 .
[258] Alex Waibel,et al. Multimodal interfaces for multimedia information agents , 1997 .
[259] Juergen Luettin,et al. Active Shape Models for Visual Speech Feature Extraction , 1996 .
[260] R. W. Sutherst,et al. Predicting the survival of immigrant insect pests in new environments. , 1991 .
[261] Christian A. Rees,et al. Molecular portraits of human breast tumours , 2000, Nature.
[262] Nikola Kasabov,et al. Rules of chaotic behaviour extracted from a fuzzy-neural network , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).
[263] Nikola K. Kasabov,et al. Ensembles of EFuNNs: an architecture for a multimodule classifier , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).
[264] F C Hoppensteadt,et al. Intermittent chaos, self-organization, and learning from synchronous synaptic activity in model neuron networks. , 1989, Proceedings of the National Academy of Sciences of the United States of America.
[265] Antoine Guisan,et al. Predictive habitat distribution models in ecology , 2000 .
[266] Gürsel Serpen,et al. The Simultaneous Recurrent Neural Network for Addressing the Scaling Problem in Static Optimization , 2001, Int. J. Neural Syst..
[267] T Poggio,et al. Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks , 1990, Science.
[268] T. Gelder,et al. Mind as Motion: Explorations in the Dynamics of Cognition , 1995 .
[269] Nikola K. Kasabov,et al. NFI: a neuro-fuzzy inference method for transductive reasoning , 2005, IEEE Transactions on Fuzzy Systems.
[270] Jong-Hwan Kim,et al. Quantum-Inspired Evolutionary Algorithm-Based Face Verification , 2003, GECCO.
[271] Piero P. Bonissone,et al. Automated fuzzy knowledge base generation and tuning , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.
[272] Xin Yao,et al. Evolutionary ensembles with negative correlation learning , 2000, IEEE Trans. Evol. Comput..
[273] Nikola Kasabov,et al. Evolving ontologies for intelligent decision support , 2006, Fuzzy Logic and the Semantic Web.
[274] N. Kasabov,et al. Transductive modeling with GA parameter optimization , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[275] L O Hall,et al. Review of MR image segmentation techniques using pattern recognition. , 1993, Medical physics.
[276] Xin Yao,et al. Evolving Neural Network Ensembles by Minimization of Mutual Information , 2004, Int. J. Hybrid Intell. Syst..
[277] David G. Stork,et al. Speechreading by Humans and Machines , 1996 .
[278] Nikola K. Kasabov,et al. TWRBF - Transductive RBF Neural Network with Weighted Data Normalization , 2004, ICONIP.
[279] Shaoning Pang,et al. Transductive support vector machines and applications in bioinformatics for promoter recognition , 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.
[280] Roberto Brunelli,et al. Person identification using multiple cues , 1995, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[281] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[282] T. Deacon. Human Brain Evolution: I. Evolution of Language Circuits , 1988 .
[283] Waleed H. Abdulla,et al. Reduced feature-set based parallel CHMM speech recognition systems , 2003, Inf. Sci..
[284] M. H. Royer,et al. Application of high-resolution weather data to pest risk assessment1 , 1991 .
[285] Nikola K. Kasabov,et al. Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities , 2007, Challenges for Computational Intelligence.
[286] Irena Koprinska,et al. Evolving fuzzy neural network for camera operations recognition , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.
[287] M. Tomita. Whole-cell simulation: a grand challenge of the 21st century. , 2001, Trends in biotechnology.
[288] Eric O. Postma,et al. Discovering the Visual Signature of Painters , 2000 .
[289] Shigeo Abe,et al. Incremental learning of feature space and classifier for face recognition , 2005, Neural Networks.
[290] G. Koch,et al. Influence of range of renal function and liver disease on predictability of creatinine clearance , 1981, Clinical pharmacology and therapeutics.
[291] Pierre Comon. Independent component analysis - a new concept? signal processing , 1994 .
[292] Frank C. Hoppensteadt,et al. An introduction to the mathematics of neurons , 1986 .
[293] Van Hulle MM. Kernel-Based Equiprobabilistic Topographic Map Formation. , 1998, Neural computation.
[294] Nikola K. Kasabov,et al. Evolutionary Computation For On-Line And Off-Line Parameter Tuning Of Evolving Fuzzy Neural Networksc , 2004, Int. J. Comput. Intell. Appl..
[295] Trevor P Martin. Fuzzy Logic and the Semantic Web , 2005, Capturing Intelligence.
[296] S. B. Kater,et al. Calcium regulation of the neuronal growth cone , 1988, Trends in Neurosciences.
[297] Cockcroft Dw,et al. Prediction of Creatinine Clearance from Serum Creatinine , 1976 .
[298] Gerald Sommer,et al. An integrated architecture for learning of reactive behaviors based on dynamic cell structures , 1997, Robotics Auton. Syst..
[299] W. Pitts,et al. A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.
[300] John C. Platt. A Resource-Allocating Network for Function Interpolation , 1991, Neural Computation.
[301] Shaoning Pang,et al. Inductive vs transductive inference, global vs local models: SVM, TSVM, and SVMT for gene expression classification problems , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[302] J. Jarrett,et al. Introduction to the Practice of Statistics , 2004 .
[303] N. Kasabov,et al. Evolving Connectionist Systems Based Role Allocation of Robots for Soccer Playing , 2005, Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005..
[304] Nikola K. Kasabov,et al. DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..
[305] Arnaud Delorme,et al. Face identification using one spike per neuron: resistance to image degradations , 2001, Neural Networks.
[306] Nikola K. Kasabov,et al. A Preliminary Study on Negative Correlation Learning via Correlation-Corrected Data (NCCD) , 2005, Neural Processing Letters.
[307] S. Grossberg. On learning and energy-entropy dependence in recurrent and nonrecurrent signed networks , 1969 .
[308] Hitoshi Iba,et al. Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation) , 2006 .
[309] B. Moore,et al. Suggested formulae for calculating auditory-filter bandwidths and excitation patterns. , 1983, The Journal of the Acoustical Society of America.
[310] Marcel J. T. Reinders,et al. A Comparison of Genetic Network Models , 2000, Pacific Symposium on Biocomputing.