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Statistical pattern recognition: A review

by Anil K. Jain, Robert P. W. Duin, Jianchang Mao - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2000
"... The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques ..."
Abstract - Cited by 1035 (30 self) - Add to MetaCart
The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network

A tutorial on support vector machines for pattern recognition

by Christopher J. C. Burges - Data Mining and Knowledge Discovery , 1998
"... The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SV ..."
Abstract - Cited by 3393 (12 self) - Add to MetaCart
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.

Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features

by Wolfgang Kabsch, Christian Sander , 1983
"... For a successful analysis of the relation between amino acid sequence and protein structure, an unambiguous and physically meaningful definition of secondary structure is essential. We have developed a set of simple and physically motivated criteria for secondary structure, programmed as a pattern-r ..."
Abstract - Cited by 2096 (5 self) - Add to MetaCart
For a successful analysis of the relation between amino acid sequence and protein structure, an unambiguous and physically meaningful definition of secondary structure is essential. We have developed a set of simple and physically motivated criteria for secondary structure, programmed as a pattern-recognition

PATTERN RECOGNITION

by unknown authors
"... Pattern recognition stems from the need for automated ..."
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Pattern recognition stems from the need for automated

Pattern Recognition

by Timothy Slivka , 2010
"... Closely related to the concept of Machine Learning, Pattern Recognition is the assignment of an output value, termed a label, to a given input value, termed an instance. This is achieved through the application of an algorithm, which usually falls into one of two general categories (although mixed t ..."
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Closely related to the concept of Machine Learning, Pattern Recognition is the assignment of an output value, termed a label, to a given input value, termed an instance. This is achieved through the application of an algorithm, which usually falls into one of two general categories (although mixed

THIRTY YEARS OF GRAPH MATCHING IN PATTERN RECOGNITION

by D. Conte , P. Foggia , C. Sansone, M. Vento , 2004
"... A recent paper posed the question: "Graph Matching: What are we really talking about?". Far from providing a definite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition field. To this aim two taxonomies are presented ..."
Abstract - Cited by 238 (1 self) - Add to MetaCart
A recent paper posed the question: "Graph Matching: What are we really talking about?". Far from providing a definite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition field. To this aim two taxonomies are presented

pattern recognition

by unknown authors
"... concerned with recognizing patterns, particularly visual and sound patterns. It is central to optical character recognition (OCR), voice recognition, and handwriting recognition. 5 Definition: ..."
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concerned with recognizing patterns, particularly visual and sound patterns. It is central to optical character recognition (OCR), voice recognition, and handwriting recognition. 5 Definition:

Support Vector Machines for Multi-Class Pattern Recognition

by J. Weston, C. Watkins , 1999
"... . The solution of binary classification problems using support vector machines (SVMs) is well developed, but multi-class problems with more than two classes have typically been solved by combining independently produced binary classifiers. We propose a formulation of the SVM that enables a multi-cla ..."
Abstract - Cited by 207 (6 self) - Add to MetaCart
-class pattern recognition problem to be solved in a single optimisation. We also propose a similar generalization of linear programming machines. We report experiments using bench-mark datasets in which these two methods achieve a reduction in the number of support vectors and kernel calculations needed.

Pattern Recognition

by unknown authors
"... journal homepage: www.elsevier.com/locate/pr On-line hand-drawn electric circuit diagram recognition using 2D ..."
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journal homepage: www.elsevier.com/locate/pr On-line hand-drawn electric circuit diagram recognition using 2D

Pattern Recognition

by Rui Xia, Tao Wang, Xuelei Hu, Shoushan Li, Chengqing Zong
"... cqzong ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
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