The Science of Pattern Recognition. Achievements and Perspectives

Automatic pattern recognition is usually considered as an engineering area which focusses on the development and evaluation of systems that imitate or assist humans in their ability of recognizing patterns. It may, however, also be considered as a science that studies the faculty of human beings (and possibly other biological systems) to discover, distinguish, characterize patterns in their environment and accordingly identify new observations. The engineering approach to pattern recognition is in this view an attempt to build systems that simulate this phenomenon. By doing that, scientific understanding is gained of what is needed in order to recognize patterns, in general.

[1]  Virendrakumar C. Bhavsar,et al.  Can a vector space based learning model discover inductive class generalization in a symbolic environment? , 1995, Pattern Recognit. Lett..

[2]  Ana L. N. Fred,et al.  Evidence Accumulation Clustering Based on the K-Means Algorithm , 2002, SSPR/SPR.

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

[4]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[5]  D. Tax,et al.  The dissimilarity representation , a basis for a domain-based pattern recognition ? , 1990 .

[6]  Horst Bunke,et al.  On Not Making Dissimilarities Euclidean , 2004, SSPR/SPR.

[7]  Horst Bunke,et al.  Towards Bridging the Gap between Statistical and Structural Pattern Recognition: Two New Concepts in Graph Matching , 2001, ICAPR.

[8]  David H. Wolpert,et al.  The Mathematics of Generalization: The Proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning , 1994 .

[9]  Mark A. Pitt,et al.  Advances in Minimum Description Length: Theory and Applications , 2005 .

[10]  R. Duin,et al.  The dissimilarity representation for pattern recognition , a tutorial , 2009 .

[11]  Horst Bunke,et al.  Recent developments in graph matching , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[12]  Thorsten Joachims,et al.  Transductive Learning via Spectral Graph Partitioning , 2003, ICML.

[13]  Enrique Vidal,et al.  Computation of Normalized Edit Distance and Applications , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Edwin R. Hancock,et al.  Pattern Vectors from Algebraic Graph Theory , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Peter A. Flach,et al.  Abduction and induction: essays on their relation and integration , 2000 .

[16]  Glenn Fung,et al.  A Feature Selection Newton Method for Support Vector Machine Classification , 2004, Comput. Optim. Appl..

[17]  Anil K. Jain,et al.  39 Dimensionality and sample size considerations in pattern recognition practice , 1982, Classification, Pattern Recognition and Reduction of Dimensionality.

[18]  R E Bolinger,et al.  The science of "pattern recognition". , 1975, JAMA.

[19]  Stephen Wolfram,et al.  A New Kind of Science , 2003, Artificial Life.

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

[21]  Klaus Obermayer,et al.  Bayesian Transduction , 1999, NIPS.

[22]  Václav Hlavác,et al.  Ten Lectures on Statistical and Structural Pattern Recognition , 2002, Computational Imaging and Vision.

[23]  Jan M. Van Campenhout,et al.  On the Possible Orderings in the Measurement Selection Problem , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[24]  O. Firschein,et al.  Syntactic pattern recognition and applications , 1983, Proceedings of the IEEE.

[25]  J. Wade Davis,et al.  Statistical Pattern Recognition , 2003, Technometrics.

[26]  Robert P. W. Duin,et al.  Four Scientific Approaches to Pattern Recognition , 2001 .

[27]  Ana L. N. Fred,et al.  Data clustering using evidence accumulation , 2002, Object recognition supported by user interaction for service robots.

[28]  T. Ho,et al.  Data Complexity in Pattern Recognition , 2006 .

[29]  M. Stone.  Cross-validation:a review 2 , 1978 .

[30]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[31]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[32]  T. Kuhn,et al.  The Structure of Scientific Revolutions. , 1964 .

[33]  A. G. Arkad'ev,et al.  Computers and pattern recognition , 1967 .

[34]  Lev Goldfarb,et al.  On the foundations of intelligent processes - I. An evolving model for pattern learning , 1990, Pattern Recognit..

[35]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[36]  Fabio Roli,et al.  A note on core research issues for statistical pattern recognition , 2002, Pattern Recognit. Lett..

[37]  T. Fine,et al.  The Emergence of Probability , 1976 .

[38]  Robert P. W. Duin,et al.  Combining Dissimilarity-Based One-Class Classifiers , 2004, Multiple Classifier Systems.

[39]  Klaus-Robert Müller,et al.  Feature Discovery in Non-Metric Pairwise Data , 2004, J. Mach. Learn. Res..

[40]  Satosi Watanabe,et al.  Pattern Recognition: Human and Mechanical , 1985 .

[41]  Peter A. Flach,et al.  Abduction and Induction , 2000 .

[42]  Richard E. Neapolitan,et al.  Probabilistic reasoning in expert systems - theory and algorithms , 2012 .

[43]  T. Subba Rao,et al.  Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB , 2004 .

[44]  Robert P. W. Duin,et al.  The Dissimilarity Representation for Pattern Recognition - Foundations and Applications , 2005, Series in Machine Perception and Artificial Intelligence.

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

[46]  V. Vapnik.  Estimation of Dependences Based on Empirical Data , 2006 .

[47]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

[48]  Kenneth M. Sayre,et al.  Recognition: A Study in the Philosophy of Artificial Intelligence by Kenneth M. Sayre (review) , 1965 .

[49]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1999, Innovations in Bayesian Networks.

[50]  Lev Goldfarb,et al.  Why Classical Models for Pattern Recognition are Not Pattern Recognition Models , 1999 .

[51]  David M. J. Tax,et al.  One-class classification , 2001 .

[52]  Leonid I. Perlovsky,et al.  Conundrum of Combinatorial Complexity , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[53]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[54]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[55]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[56]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[57]  Thomas M. Cover,et al.  Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition , 1965, IEEE Trans. Electron. Comput..

[58]  Tin Kam Ho,et al.  Complexity Measures of Supervised Classification Problems , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[59]  Robert P. W. Duin,et al.  The characterization of classification problems by classifier disagreements , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[60]  Ana L. N. Fred,et al.  Robust data clustering , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[61]  Robert P. W. Duin,et al.  Open Issues in Pattern Recognition , 2005, CORES.

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

[63]  Bernard Haasdonk,et al.  Feature space interpretation of SVMs with indefinite kernels , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[64]  R. C. Williamson,et al.  Classification on proximity data with LP-machines , 1999 .

[65]  Sholom M. Weiss,et al.  Computer Systems That Learn , 1990 .

[66]  Mehmet H. Göker.  Designing Industrial Case-Based Reasoning Applications , 2004, ECCBR.

[67]  R. Michalski.  Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning , 2004, Machine Learning.

[68]  Robert P. W. Duin,et al.  A Generalized Kernel Approach to Dissimilarity-based Classification , 2002, J. Mach. Learn. Res..

[69]  Shimon Edelman,et al.  Representation and recognition in vision , 1999 .

[70]  Sanjeev R. Kulkarni,et al.  Reliable Reasoning: Induction and Statistical Learning Theory , 2007 .

[71]  Horst Bunke,et al.  A graph distance metric based on the maximal common subgraph , 1998, Pattern Recognit. Lett..

[72]  Edwin R. Hancock,et al.  Structural Matching by Discrete Relaxation , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

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

[74]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[75]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[76]  Christian P. Robert,et al.  The Bayesian choice , 1994 .

[77]  Robert A. Lordo,et al.  Learning from Data: Concepts, Theory, and Methods , 2001, Technometrics.

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

[79]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[80]  Oleg Golubitsky,et al.  What Is a Structural Measurement Process , 2001 .

[81]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[82]  Kenneth M. Sayre,et al.  Recognition: A Study in the Philosophy of Artificial Intelligence , 1966 .

[83]  Michael I. Jordan.  Learning in Graphical Models , 1999, NATO ASI Series.

[84]  László Györfi,et al.  A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.

[85]  Javier M. Moguerza,et al.  Combining Kernel Information for Support Vector Classification , 2004, Multiple Classifier Systems.

[86]  J. Kacprzyk,et al.  Advances in the Dempster-Shafer theory of evidence , 1994 .

[87]  Ralph Bergmann,et al.  Developing Industrial Case-Based Reasoning Applications , 1999, Lecture Notes in Computer Science.

[88]  Alexander J. Smola,et al.  Learning with non-positive kernels , 2004, ICML.