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» Minimal Kernel Classifiers
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CVPR
2008
IEEE
16 years 8 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
ICMCS
2009
IEEE
115views Multimedia» more  ICMCS 2009»
15 years 4 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»
15 years 6 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 9 months 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
KDD
2000
ACM
133views Data Mining» more  KDD 2000»
15 years 10 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