Statistical Pattern Recognition: A Review
The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
- J. Kittler, M. Hatef, R. Duin, Jiri Matas
- Computer Science, BusinessProceedings of 13th International Conference on…
- 25 August 1996
We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound…
Support vector domain description
Support Vector Data Description
The Support Vector Data Description (SVDD) is presented which obtains a spherically shaped boundary around a dataset and analogous to the Support Vector Classifier it can be made flexible by using other kernel functions.
Decision templates for multiple classifier fusion: an experimental comparison
A Generalized Kernel Approach to Dissimilarity-based Classification
It is shown that other, more global classification techniques are preferable to the nearest neighbor rule, in such cases when dissimilarities used in practice are far from ideal and the performance of the nearest neighbors rule suffers from its sensitivity to noisy examples.
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
- M. Loog, R. Duin, Reinhold Häb-Umbach
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 1 July 2001
We derive a class of computationally inexpensive linear dimension reduction criteria by introducing a weighted variant of the well-known K-class Fisher criterion associated with linear discriminant…
Linear dimensionality reduction via a heteroscedastic extension of LDA: the Chernoff criterion
An eigenvector-based heteroscedastic linear dimension reduction (LDR) technique for multiclass data that successfully extends the well-known linear discriminant analysis (LDA) and combines separation information present in the class mean as well as the class covariance matrices.
The Dissimilarity Representation for Pattern Recognition - Foundations and Applications
This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and expensive and expensive process of classification and classification-based visualization of spaces and dissimilarity measures.
Data domain description using support vectors
This paper introduces a new method for data domain description, inspired by the Support Vector Machine by V.Vapnik, which computes a sphere shaped decision boundary with minimal volume around a set of objects and contains support vectors describing the sphere boundary.