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» Objective Functions for Feature Discrimination
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131
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BMCBI
2008
108views more  BMCBI 2008»
15 years 2 months ago
A nonparametric model for quality control of database search results in shotgun proteomics
Background: Analysis of complex samples with tandem mass spectrometry (MS/MS) has become routine in proteomic research. However, validation of database search results creates a bo...
Jiyang Zhang, Jianqi Li, Xin Liu, Hongwei Xie, Yun...
88
Voted
ICPR
2000
IEEE
15 years 7 months ago
Two-Stage Computational Cost Reduction Algorithm Based on Mahalanobis Distance Approximations
For many pattern recognition methods, high recognition accuracy is obtained at very high expense of computational cost. In this paper, a new algorithm that reduces the computation...
Fang Sun, Shinichiro Omachi, Nei Kato, Hirotomo As...
169
Voted
JMLR
2012
13 years 5 months ago
Online Incremental Feature Learning with Denoising Autoencoders
While determining model complexity is an important problem in machine learning, many feature learning algorithms rely on cross-validation to choose an optimal number of features, ...
Guanyu Zhou, Kihyuk Sohn, Honglak Lee
133
Voted
CVPR
2010
IEEE
15 years 7 months ago
Local Features Are Not Lonely - Laplacian Sparse Coding for Image Classification
Sparse coding which encodes the original signal in a sparse signal space, has shown its state-of-the-art performance in the visual codebook generation and feature quantization pro...
Shenghua Gao, Wai-Hung Tsang, Liang-Tien Chia, Pei...
146
Voted
ICDM
2007
IEEE
129views Data Mining» more  ICDM 2007»
15 years 4 months ago
Feature Selection for Nonlinear Kernel Support Vector Machines
An easily implementable mixed-integer algorithm is proposed that generates a nonlinear kernel support vector machine (SVM) classifier with reduced input space features. A single ...
Olvi L. Mangasarian, Gang Kou