On pattern classification algorithms introduction and survey

This paper attempts to lay bare the underlying ideas used in various pattern classification algorithms reported in the literature. It is shown that these algorithms can be classified according to the type of input information required and that the techniques of estimation, decision, and optimization theory can be used to effectively derive known as well as new results.

[1]  S. Agmon The Relaxation Method for Linear Inequalities , 1954, Canadian Journal of Mathematics.

[2]  D J Rogers,et al.  A Computer Program for Classifying Plants. , 1960, Science.

[3]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[4]  Marvin Minsky,et al.  Steps toward Artificial Intelligence , 1995, Proceedings of the IRE.

[5]  H. Raiffa,et al.  Applied Statistical Decision Theory. , 1961 .

[6]  Norman M. Abramson,et al.  Learning to recognize patterns in a random environment , 1962, IRE Trans. Inf. Theory.

[7]  T. W. Anderson,et al.  Classification into two Multivariate Normal Distributions with Different Covariance Matrices , 1962 .

[8]  Albert B Novikoff,et al.  ON CONVERGENCE PROOFS FOR PERCEPTRONS , 1963 .

[9]  C. Hugh Mays,et al.  Effects of Adaptation Parameters on Convergence Time and Tolerance for Adaptive Threshold Elements , 1964, IEEE Trans. Electron. Comput..

[10]  Alan G. Konheim,et al.  Linear and Nonlinear Methods in Pattern Classification , 1964, IBM J. Res. Dev..

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

[12]  K R KAPLAN,et al.  ANALYSIS OF MARKOV CHAIN MODELS OF ADAPTIVE PROCESSES. AMRL-TR-65-3. , 1965, AMRL-TR. Aerospace Medical Research Laboratories.

[13]  E. Wong,et al.  Iterative Synthesis of Threshold Functions , 1965 .

[14]  J. B. Rosen Pattern separation by convex programming , 1965 .

[15]  Paul W. Cooper,et al.  Quadratic discriminant functions in pattern recognition (Corresp.) , 1965, IEEE Trans. Inf. Theory.

[16]  J. Spragins,et al.  A note on the iterative application of Bayes' rule , 1965, IEEE Trans. Inf. Theory.

[17]  O. Mangasarian Linear and Nonlinear Separation of Patterns by Linear Programming , 1965 .

[18]  Daniel G. Keehn,et al.  A note on learning for Gaussian properties , 1965, IEEE Trans. Inf. Theory.

[19]  Jack Sklansky Threshold training of two-mode signal detection , 1965, IEEE Trans. Inf. Theory.

[20]  Donald B. Brick,et al.  Wiener's Nonlinear Expansion Procedure Applied to Cybernetic Problems , 1965, IEEE Trans. Syst. Sci. Cybern..

[21]  Y. Ho,et al.  A Class of Iterative Procedures for Linear Inequalities , 1966 .

[22]  John C. Hancock,et al.  Nonsupervised sequential classification and recognition of patterns , 1966, IEEE Trans. Inf. Theory.

[23]  D. W. Peterson,et al.  A method of finding linear discriminant functions for a class of performance criteria , 1966, IEEE Trans. Inf. Theory.

[24]  Richard O. Duda,et al.  Pattern Classification by Iteratively Determined Linear and Piecewise Linear Discriminant Functions , 1966, IEEE Trans. Electron. Comput..

[25]  George Nagy,et al.  Self-corrective character recognition system , 1966, IEEE Trans. Inf. Theory.

[26]  R. Kashyap,et al.  Recovery of functions from noisy measurements taken at randomly selected points and its application to pattern classification , 1966 .

[27]  John D. Spragins,et al.  Learning without a teacher , 1966, IEEE Trans. Inf. Theory.

[28]  J. Sklansky,et al.  Learning systems for automatic control , 1966 .

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

[30]  Yi-Tzuu Chien ON THE FINITE STOPPING RULES AND NONPARAMETRIC TECHNIQUES IN A FEATURE-ORDERED SEQUENTIAL RECOGNITION SYSTEM , 1967 .

[31]  King-Sun Fu,et al.  A Dynamic Programming Approach to Sequential Pattern Recognition , 1967, IEEE Trans. Electron. Comput..

[32]  Stanley C. Fralick,et al.  Learning to recognize patterns without a teacher , 1967, IEEE Trans. Inf. Theory.

[33]  Colin C. Blaydon,et al.  RECURSIVE ALGORITHMS FOR PATTERN CLASSIFICATION. , 1967 .

[34]  Y. Ho,et al.  On the self-learning scheme of Nagy and Shelton , 1967 .

[35]  T. Wagner,et al.  A mean-square performance criterion for adaptive pattern classification systems , 1967, IEEE Transactions on Automatic Control.

[36]  George Nagy,et al.  State of the art in pattern recognition , 1968 .

[37]  J. H. Munson,et al.  Experiments with Highleyman's Data , 1968, IEEE Transactions on Computers.