Efficient sequential and batch learning artificial neural network methods for classification problems

[1]  Eric B. Baum,et al.  Supervised Learning of Probability Distributions by Neural Networks , 1987, NIPS.

[2]  M. J. D. Powell,et al.  Radial basis functions for multivariable interpolation: a review , 1987 .

[3]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  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.

[5]  W R Taylor,et al.  SSAP: sequential structure alignment program for protein structure comparison. , 1996, Methods in enzymology.

[6]  Sayan Mukherjee,et al.  Molecular classification of multiple tumor types , 2001, ISMB.

[7]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[8]  Richard Lippmann,et al.  Neural Network Classifiers Estimate Bayesian a posteriori Probabilities , 1991, Neural Computation.

[9]  J. Downing,et al.  Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. , 2002, Cancer cell.

[10]  Narasimhan Sundararajan,et al.  An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[12]  Peter L. Bartlett,et al.  The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network , 1998, IEEE Trans. Inf. Theory.

[13]  Constantin F. Aliferis,et al.  A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis , 2004, Bioinform..

[14]  Guang-Bin Huang,et al.  Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions , 1998, IEEE Trans. Neural Networks.

[15]  Xin Zhou,et al.  LS Bound based gene selection for DNA microarray data , 2005, Bioinform..

[16]  J. Sudbø,et al.  Gene-expression profiles in hereditary breast cancer. , 2001, The New England journal of medicine.

[17]  Peter Bühlmann,et al.  Boosting for Tumor Classification with Gene Expression Data , 2003, Bioinform..

[18]  T. Poggio,et al.  Prediction of central nervous system embryonal tumour outcome based on gene expression , 2002, Nature.

[19]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[20]  Zhiyong Gao,et al.  A novel method for beat-to-beat detection of ventricular late potentials , 2001, IEEE Transactions on Biomedical Engineering.

[21]  Gaston H. Gonnet,et al.  Evaluation Measures of Multiple Sequence Alignments , 2000, J. Comput. Biol..

[22]  Shili Lin,et al.  Class discovery and classification of tumor samples using mixture modeling of gene expression data - a unified approach , 2004, Bioinform..

[23]  J. Berger.  Statistical Decision Theory and Bayesian Analysis , 1988 .

[24]  Russ B. Altman,et al.  Missing value estimation methods for DNA microarrays , 2001, Bioinform..

[25]  Jae Won Lee,et al.  An extensive comparison of recent classification tools applied to microarray data , 2004, Comput. Stat. Data Anal..

[26]  Robert A. Schowengerdt,et al.  Remote sensing, models, and methods for image processing , 1997 .

[27]  Narasimhan Sundararajan,et al.  A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation , 2005, IEEE Transactions on Neural Networks.

[28]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[29]  Edward R. Dougherty,et al.  How many samples are needed to build a classifier: a general sequential approach , 2005, Bioinform..

[30]  J. Welsh,et al.  Molecular classification of human carcinomas by use of gene expression signatures. , 2001, Cancer research.

[31]  Chee Kheong Siew,et al.  Extreme learning machine: RBF network case , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..

[32]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[33]  Jiawei Han,et al.  Cancer classification using gene expression data , 2003, Inf. Syst..

[34]  Wray L. Buntine,et al.  Computing second derivatives in feed-forward networks: a review , 1994, IEEE Trans. Neural Networks.

[35]  E. Boerwinkle,et al.  Feature (gene) selection in gene expression-based tumor classification. , 2001, Molecular genetics and metabolism.

[36]  Yi Liao,et al.  Neural Networks for Pattern Classification and Universal Approximation , 2002 .

[37]  B. Margolin,et al.  An Analysis of Variance for Categorical Data , 1971 .

[38]  Marvin Minsky,et al.  Perceptrons: An Introduction to Computational Geometry , 1969 .

[39]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[40]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[41]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[42]  Nicolaos B. Karayiannis,et al.  Reformulated radial basis neural networks trained by gradient descent , 1999, IEEE Trans. Neural Networks.

[43]  Gert Pfurtscheller,et al.  Automatic differentiation of multichannel EEG signals , 2001, IEEE Transactions on Biomedical Engineering.

[44]  Li Gang,et al.  An artificial-intelligence approach to ECG analysis , 2000 .

[45]  M. Ringnér,et al.  Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.

[46]  Chee Kheong Siew,et al.  Can threshold networks be trained directly? , 2006, IEEE Transactions on Circuits and Systems II: Express Briefs.

[47]  Guang-Bin Huang,et al.  Learning capability and storage capacity of two-hidden-layer feedforward networks , 2003, IEEE Trans. Neural Networks.

[48]  Christian A. Rees,et al.  Systematic variation in gene expression patterns in human cancer cell lines , 2000, Nature Genetics.

[49]  Werner Dubitzky,et al.  A Practical Approach to Microarray Data Analysis , 2003, Springer US.

[50]  Vladimir Pavlovic,et al.  RankGene: identification of diagnostic genes based on expression data , 2003, Bioinform..

[51]  A. L. Tarca,et al.  A robust neural networks approach for spatial and intensity-dependent normalization of cDNA microarray data , 2005, Bioinform..

[52]  Donald F. Specht,et al.  Generation of Polynomial Discriminant Functions for Pattern Recognition , 1967, IEEE Trans. Electron. Comput..

[53]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[54]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[55]  L. Acosta,et al.  Brain maturation estimation using neural classifier , 1995, IEEE Transactions on Biomedical Engineering.

[56]  Andy Brass,et al.  A RAPID algorithm for sequence database comparisons: application to the identification of vector contamination in the EMBL databases , 1999, Bioinform..

[57]  William Nick Street,et al.  An adaptive resource-allocating network for automated detection, segmentation, and classification of breast cancer nuclei topic area: image processing and recognition , 2003, IEEE Trans. Neural Networks.

[58]  Thomas A. Darden,et al.  Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method , 2001, Bioinform..

[59]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[60]  Nicolaos B. Karayiannis,et al.  Growing radial basis neural networks: merging supervised and unsupervised learning with network growth techniques , 1997, IEEE Trans. Neural Networks.

[61]  Hervé Bourlard,et al.  Continuous speech recognition using multilayer perceptrons with hidden Markov models , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[62]  C. K. Chow,et al.  An optimum character recognition system using decision functions , 1957, IRE Trans. Electron. Comput..

[63]  Seong-Whan Lee,et al.  Advances in Handwriting Recognition , 1999, Series in Machine Perception and Artificial Intelligence.

[64]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[65]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[66]  Heinz Ulrich Hoppe,et al.  An automatic sequential recognition method for cortical auditory evoked potentials , 2001, IEEE Transactions on Biomedical Engineering.

[67]  Lee A. Feldkamp,et al.  Decoupled extended Kalman filter training of feedforward layered networks , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[68]  J. Ross Quinlan,et al.  Learning Efficient Classification Procedures and Their Application to Chess End Games , 1983 .

[69]  C. Siew,et al.  Extreme Learning Machine with Randomly Assigned RBF Kernels , 2005 .

[71]  M. Inés Torres,et al.  Pattern recognition and applications , 2000 .

[72]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..

[73]  Zheng Rong Yang,et al.  Mining gene expression data based on template theory , 2004, Bioinform..

[74]  Mohan M. Trivedi,et al.  Image Analysis Applications , 1990 .

[75]  S. Billings,et al.  Fast orthogonal identification of nonlinear stochastic models and radial basis function neural networks , 1996 .

[76]  StatnikovAlexander,et al.  A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis , 2005 .

[77]  Todd,et al.  Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning , 2002, Nature Medicine.

[78]  Yoonkyung Lee,et al.  Classification of Multiple Cancer Types by Multicategory Support Vector Machines Using Gene Expression Data , 2003, Bioinform..

[79]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[80]  Cathy H. Wu,et al.  Neural networks and genome informatics , 2000 .

[81]  Marcel Dettling,et al.  BagBoosting for tumor classification with gene expression data , 2004, Bioinform..

[82]  E. Lander,et al.  Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.

[83]  Stanislaw Osowski,et al.  ECG beat recognition using fuzzy hybrid neural network , 2001, IEEE Trans. Biomed. Eng..

[84]  E. Lander.  Array of hope , 1999, Nature Genetics.

[85]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[86]  Alexander H. Waibel,et al.  Connectionist Architectures for Multi-Speaker Phoneme Recognition , 1989, NIPS.

[87]  Shin'ichi Tamura,et al.  Capabilities of a four-layered feedforward neural network: four layers versus three , 1997, IEEE Trans. Neural Networks.

[88]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[89]  Lutz Prechelt,et al.  A Set of Neural Network Benchmark Problems and Benchmarking Rules , 1994 .

[90]  Ian T. Nabney,et al.  Efficient Training Of Rbf Networks For Classification , 2004, Int. J. Neural Syst..

[91]  M. Fieschi,et al.  Introduction to Clinical Informatics , 1996, Health Informatics Series.

[92]  Nicolaos B. Karayiannis,et al.  On the construction and training of reformulated radial basis function neural networks , 2003, IEEE Trans. Neural Networks.

[93]  Héctor Pomares,et al.  Time series analysis using normalized PG-RBF network with regression weights , 2002, Neurocomputing.

[94]  Masafumi Hagiwara,et al.  Theoretical derivation of momentum term in back-propagation , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[95]  Lee A. Feldkamp,et al.  Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks , 1994, IEEE Trans. Neural Networks.

[96]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[97]  Lipo Wang,et al.  Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[98]  D. Botstein,et al.  Diversity of gene expression in adenocarcinoma of the lung , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[99]  Narasimhan Sundararajan,et al.  Fully complex extreme learning machine , 2005, Neurocomputing.

[100]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[101]  Sung Yang Bang,et al.  An Efficient Method to Construct a Radial Basis Function Neural Network Classifier , 1997, Neural Networks.

[102]  Paramasivan Saratchandran,et al.  Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm , 1998, IEEE Trans. Neural Networks.

[103]  David Casasent,et al.  Radial basis function neural networks for nonlinear Fisher discrimination and Neyman-Pearson classification , 2003, Neural Networks.

[104]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[105]  Visakan Kadirkamanathan,et al.  A Function Estimation Approach to Sequential Learning with Neural Networks , 1993, Neural Computation.

[106]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[107]  E. Lander,et al.  MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia , 2002, Nature Genetics.

[108]  J. Mesirov,et al.  Chemosensitivity prediction by transcriptional profiling , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[109]  David A. Fenstermacher,et al.  Introduction to bioinformatics , 2005, J. Assoc. Inf. Sci. Technol..

[110]  John C. Platt.  A Resource-Allocating Network for Function Interpolation , 1991, Neural Computation.

[111]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[112]  Sharath Pankanti,et al.  Guide to Biometrics , 2003, Springer Professional Computing.

[113]  M. Ringnér,et al.  Analyzing array data using supervised methods. , 2002, Pharmacogenomics.

[114]  Carsten Peterson,et al.  Clustering ECG complexes using Hermite functions and self-organizing maps , 2000, IEEE Trans. Biomed. Eng..

[115]  Friedhelm Schwenker,et al.  Three learning phases for radial-basis-function networks , 2001, Neural Networks.

[116]  Tao Li,et al.  A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression , 2004, Bioinform..

[117]  Julio Ortega Lopera,et al.  Improved RAN sequential prediction using orthogonal techniques , 2001, Neurocomputing.

[118]  Johan A. K. Suykens,et al.  Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction , 2004, Bioinform..

[119]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[120]  N M Luscombe,et al.  What is Bioinformatics? A Proposed Definition and Overview of the Field , 2001, Methods of Information in Medicine.

[121]  David L. Wheeler,et al.  GenBank , 2015, Nucleic Acids Res..

[122]  Wei Pan,et al.  Linear regression and two-class classification with gene expression data , 2003, Bioinform..

[123]  D. Serre.  Matrices: Theory and Applications , 2002 .

[124]  Adil M. Bagirov,et al.  New algorithms for multi-class cancer diagnosis using tumor gene expression signatures , 2003, Bioinform..

[125]  David G. Stork,et al.  Pattern Classification , 1973 .

[126]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[127]  Siegfried J. Pöppl,et al.  The 'subsequent artificial neural network' (SANN) approach might bring more classificatory power to ANN-based DNA microarray analyses , 2004, Bioinform..

[128]  Chee Kheong Siew,et al.  Real-time learning capability of neural networks , 2006, IEEE Trans. Neural Networks.

[129]  Michel Verleysen,et al.  Enhanced learning for evolutive neural architectures , 1995 .

[130]  Walter L. Ruzzo,et al.  Bayesian Classification of DNA Array Expression Data , 2000 .

[131]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[132]  Anna Esposito,et al.  Approximation of continuous and discontinuous mappings by a growing neural RBF-based algorithm , 2000, Neural Networks.

[133]  J. L. Hodges,et al.  Discriminatory Analysis - Nonparametric Discrimination: Small Sample Performance , 1952 .

[134]  J. L. Hodges,et al.  Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties , 1989 .

[135]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[136]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[137]  Li Gang,et al.  An artificial-intelligence approach to ECG analysis , 2000, IEEE Engineering in Medicine and Biology Magazine.

[138]  Sophie Lambert-Lacroix,et al.  Effective dimension reduction methods for tumor classification using gene expression data , 2003, Bioinform..

[139]  T. Golub,et al.  Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. , 2003, Cancer research.

[140]  U. Zwiener,et al.  Specific monitoring of neonatal brain function with optimized frequency bands , 2001, IEEE Engineering in Medicine and Biology Magazine.