A brief comparative study of the potentialities and limitations of machine-learning algorithms and statistical techniques
暂无分享,去创建一个
Li Eckart | Sven Eckart | Margit Enke | M. Enke | S. Eckart | L. Eckart
[1] Yong Shi,et al. A review of data-driven approaches for prediction and classification of building energy consumption , 2018 .
[2] Arthur Zimek,et al. Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection , 2015, ACM Trans. Knowl. Discov. Data.
[3] Marian Verhelst,et al. A Review on Internet of Things Solutions for Intelligent Energy Control in Buildings for Smart City Applications , 2017 .
[4] Teuvo Kohonen,et al. The self-organizing map , 1990, Neurocomputing.
[5] Shiori Kuramoto,et al. Visualization of topographical internal representation of learning robots , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).
[6] Fernando Bação,et al. Self-organizing Maps as Substitutes for K-Means Clustering , 2005, International Conference on Computational Science.
[7] M. F. Ghazali,et al. A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel , 2018 .
[8] R. H. Myers. Classical and modern regression with applications , 1986 .
[9] R. Rastogi,et al. CURE: An Efficient Clustering Algorithm for Large Databases , 1998, SIGMOD Conference.
[10] J V Tu,et al. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. , 1996, Journal of clinical epidemiology.
[11] Theofilos A. Papadopoulos,et al. Pattern recognition algorithms for electricity load curve analysis of buildings , 2014 .
[12] Robert J. Kauffman,et al. Consumer Informedness and Firm Information Strategy , 2013, Inf. Syst. Res..
[13] Alán Aspuru-Guzik,et al. Accelerating the discovery of materials for clean energy in the era of smart automation , 2018, Nature Reviews Materials.
[14] Birgitta Dresp-Langley,et al. The quantization error in a Self-Organizing Map as a contrast and colour specific indicator of single-pixel change in large random patterns , 2019, Neural Networks.
[15] David West,et al. A comparison of SOM neural network and hierarchical clustering methods , 1996 .
[16] Rui Xu,et al. Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.
[17] Maurice K. Wong,et al. Algorithm AS136: A k-means clustering algorithm. , 1979 .
[18] A. V. Boikov,et al. Evaluation of bulk material behavior control method in technological units using DEM. Part 1 , 2020 .
[19] Teuvo Kohonen,et al. The self-organizing map , 1990 .
[20] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[21] Julian D. Olden,et al. Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks , 2002 .
[22] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[23] David J. Hand,et al. Mixture Models: Inference and Applications to Clustering , 1989 .
[24] Sylvain Robert,et al. State of the art in building modelling and energy performances prediction: A review , 2013 .
[25] June-Goo Lee,et al. Deep Learning in Medical Imaging: General Overview , 2017, Korean journal of radiology.
[26] Hans-Peter Kriegel,et al. OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.
[27] Sudipto Guha,et al. ROCK: A Robust Clustering Algorithm for Categorical Attributes , 2000, Inf. Syst..
[28] H. Wold,et al. On Prediction in Stationary Time Series , 1948 .
[29] Danuta Szpilko,et al. Smart city concept in the light of the literature review , 2019, Engineering Management in Production and Services.
[30] Geoffrey E. Hinton,et al. How neural networks learn from experience. , 1992, Scientific American.
[31] Delbert Dueck,et al. Clustering by Passing Messages Between Data Points , 2007, Science.
[32] Peter J. Rousseeuw,et al. Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .
[33] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[34] Teuvo Kohonen,et al. Physiological interpretationm of the self-organizing map algorithm , 1993 .
[35] F. Kujur,et al. Emotions as predictor for consumer engagement in YouTube advertisement , 2018 .
[36] D T Jones,et al. Protein secondary structure prediction based on position-specific scoring matrices. , 1999, Journal of molecular biology.
[37] G. David Garson,et al. Interpreting neural-network connection weights , 1991 .
[38] Benjamin King. Step-Wise Clustering Procedures , 1967 .
[39] S. Shen-Orr,et al. Network motifs: simple building blocks of complex networks. , 2002, Science.
[40] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[41] Klaus-Dieter Thoben,et al. "Industrie 4.0" and Smart Manufacturing - A Review of Research Issues and Application Examples , 2017, Int. J. Autom. Technol..
[42] Himer Avila-George,et al. Comparison between artificial neural network and partial least squares regression models for hardness modeling during the ripening process of Swiss-type cheese using spectral profiles , 2018 .
[43] Amir Masoud Rahmani,et al. Internet of Things applications: A systematic review , 2019, Comput. Networks.
[44] S. Voss,et al. Laminar burning velocities of low calorific and hydrogen containing fuel blends , 2017 .
[45] M. Ankerst,et al. OPTICS: ordering points to identify the clustering structure , 1999, ACM SIGMOD Conference.
[46] D. Basak,et al. Support Vector Regression , 2008 .
[47] Tian Zhang,et al. BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.
[48] Feiping Nie,et al. Large-Scale Cross-Language Web Page Classification via Dual Knowledge Transfer Using Fast Nonnegative Matrix Trifactorization , 2015, ACM Trans. Knowl. Discov. Data.
[49] Halbert White,et al. Learning in Artificial Neural Networks: A Statistical Perspective , 1989, Neural Computation.
[50] Niels G. Waller,et al. A comparison of the classification capabilities of the 1-dimensional kohonen neural network with two pratitioning and three hierarchical cluster analysis algorithms , 1998 .
[51] Sang-Hoon Kim,et al. A quantile regression approach to gaining insights for reacquition of defected customers , 2020 .
[52] Sudipto Guha,et al. CURE: an efficient clustering algorithm for large databases , 1998, SIGMOD '98.
[53] Joachim Denzler,et al. Deep learning and process understanding for data-driven Earth system science , 2019, Nature.
[54] Zeyu Wang,et al. A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models , 2017 .
[55] Barry A. Wray,et al. Determinants of relationship quality: An artificial neural network analysis , 1996 .
[56] Russell G. Death,et al. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .
[57] Melody Y. Kiang,et al. An Evaluation of Self-Organizing Map Networks as a Robust Alternative to Factor Analysis in Data Mining Applications , 2001, Inf. Syst. Res..
[58] Soteris A. Kalogirou,et al. Machine learning methods for solar radiation forecasting: A review , 2017 .
[59] David Flynn,et al. Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review , 2020, Renewable and Sustainable Energy Reviews.
[60] Gregory Vial,et al. Understanding digital transformation: A review and a research agenda , 2019, J. Strateg. Inf. Syst..
[61] M. Kearns,et al. Fairness in Criminal Justice Risk Assessments: The State of the Art , 2017, Sociological Methods & Research.
[62] J. A. Hartigan,et al. A k-means clustering algorithm , 1979 .
[63] L Leinonen,et al. Self-organized acoustic feature map in detection of fricative-vowel coarticulation. , 1993, The Journal of the Acoustical Society of America.
[64] Chin-Teng Lin,et al. A review of clustering techniques and developments , 2017, Neurocomputing.
[65] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[66] Virgilio Cruz-Machado,et al. Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems , 2019, Engineering Science and Technology, an International Journal.
[67] Simone Santini,et al. Three-dimensional planar-faced object classification with Kohonen maps , 1993 .
[68] Puneet Agrawal,et al. Understanding Emotions in Text Using Deep Learning and Big Data , 2019, Comput. Hum. Behav..
[69] Peter H. A. Sneath,et al. Numerical Taxonomy: The Principles and Practice of Numerical Classification , 1973 .
[70] Kenji Suzuki,et al. Overview of deep learning in medical imaging , 2017, Radiological Physics and Technology.
[71] Geoffrey J. McLachlan,et al. Mixture models : inference and applications to clustering , 1989 .
[72] Klaus Schulten,et al. Implementation of self-organizing neural networks for visuo-motor control of an industrial robot , 1993, IEEE Trans. Neural Networks.
[73] Ashish Dutta,et al. Kinematics-based end-effector path control of a mobile manipulator system on an uneven terrain using a two-stage Support Vector Machine , 2019, Robotica.
[74] J. MacQueen. Some methods for classification and analysis of multivariate observations , 1967 .
[75] B. B. Zaidan,et al. A review of smart home applications based on Internet of Things , 2017, J. Netw. Comput. Appl..
[76] Esa Alhoniemi,et al. Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..
[77] S. Brunak,et al. SHORT COMMUNICATION Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites , 1997 .
[78] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.
[79] Olaf Sporns,et al. Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.
[80] John Mwangi Wandeto,et al. The quantization error in a Self-Organizing Map as a contrast and colour specific indicator of single-pixel change in large random patterns , 2019, Neural Networks.
[81] Marlene Amorim,et al. Digital Transformation: A Literature Review and Guidelines for Future Research , 2018, WorldCIST.
[82] Hadi Salehi,et al. Emerging artificial intelligence methods in structural engineering , 2018, Engineering Structures.
[83] G. W. Milligan,et al. A study of standardization of variables in cluster analysis , 1988 .
[84] Yizong Cheng,et al. Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..
[85] M. Javaid,et al. Artificial Intelligence (AI) applications for COVID-19 pandemic , 2020, Diabetes & Metabolic Syndrome: Clinical Research & Reviews.