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A training algorithm for optimal margin classifiers
TLDR
A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. Expand
An Introduction to Variable and Feature Selection
TLDR
Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. Expand
Gene Selection for Cancer Classification using Support Vector Machines
TLDR
We propose a new method of gene selection utilizing Support Vector Machine methods based on Recursive Feature Elimination (RFE) and demonstrate that the genes selected by our techniques yield better classification performance and are biologically relevant to cancer. Expand
Signature Verification Using A "Siamese" Time Delay Neural Network
TLDR
This paper describes an algorithm for verification of signatures written on a pen-input tablet. Expand
Feature Extraction - Foundations and Applications
TLDR
Feature Extraction Fundamentals and Mining for Complex Models Comprising Feature Selection and Classification. Expand
Result Analysis of the NIPS 2003 Feature Selection Challenge
TLDR
The NIPS 2003 workshops included a feature selection competition on the theme of feature selection, the results of which were presented at a workshop on feature extraction, which attracted 98 participants. Expand
A Stability Based Method for Discovering Structure in Clustered Data
TLDR
We present a method for visually and quantitatively assessing the presence of structure in clustered data. Expand
Comparison of learning algorithms for handwritten digit recognition
TLDR
This paper compares the relative merits of several classi cation algorithms developed at Bell Laboratories and elsewhere for the purpose of recognizing handwritten digits. Expand
Comparison of classifier methods: a case study in handwritten digit recognition
TLDR
This paper compares the performance of several classifier algorithms on a standard database of handwritten digits with respect to training time, recognition time, and memory requirements. Expand
Learning algorithms for classification: A comparison on handwritten digit recognition
TLDR
This paper compares the performance of several classi er algorithms on a standard database of handwritten digits. Expand
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