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» Minimal Kernel Classifiers
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CVPR
2008
IEEE
15 years 11 months ago
Taylor expansion based classifier adaptation: Application to person detection
Because of the large variation across different environments, a generic classifier trained on extensive data-sets may perform sub-optimally in a particular test environment. In th...
Cha Zhang, Raffay Hamid, Zhengyou Zhang
74
Voted
ICMCS
2009
IEEE
115views Multimedia» more  ICMCS 2009»
14 years 7 months ago
A framework to detect and classify activity transitions in low-power applications
Minimizing the number of computations a low-power device makes is important to achieve long battery life. In this paper we present a framework for a low-power device to minimize t...
Jeffrey Boyd, Hari Sundaram
BMCBI
2008
88views more  BMCBI 2008»
14 years 9 months ago
Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable
Background: By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Na
Myron Peto, Andrzej Kloczkowski, Vasant Honavar, R...
INFOCOM
2012
IEEE
13 years 4 days ago
Block permutations in Boolean Space to minimize TCAM for packet classification
Packet classification is one of the major challenges in designing high-speed routers and firewalls as it involves sophisticated multi-dimensional searching. Ternary Content Address...
Rihua Wei, Yang Xu, H. Jonathan Chao
84
Voted
KDD
2000
ACM
133views Data Mining» more  KDD 2000»
15 years 1 months ago
Data selection for support vector machine classifiers
The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classifier, is formulated as a concave minimiza...
Glenn Fung, Olvi L. Mangasarian