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» Predicting Time Series with Support Vector Machines
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109
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ICDM
2008
IEEE
160views Data Mining» more  ICDM 2008»
15 years 10 months ago
Direct Zero-Norm Optimization for Feature Selection
Zero-norm, defined as the number of non-zero elements in a vector, is an ideal quantity for feature selection. However, minimization of zero-norm is generally regarded as a combi...
Kaizhu Huang, Irwin King, Michael R. Lyu
146
Voted
IVC
2006
187views more  IVC 2006»
15 years 3 months ago
Dynamics of facial expression extracted automatically from video
We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions, including AdaBoost, support vector mach...
Gwen Littlewort, Marian Stewart Bartlett, Ian R. F...
145
Voted
EPS
1995
Springer
15 years 7 months ago
An Evolutionary Programming Approach to Self-Adaptation on Finite State Machines
Evolutionary programming was first offered as an alternative method for generating artificial intelligence. Experiments were offered in which finite state machines were used to...
Lawrence J. Fogel, Peter J. Angeline, David B. Fog...
126
Voted
TKDE
2008
123views more  TKDE 2008»
15 years 3 months ago
Explaining Classifications For Individual Instances
We present a method for explaining predictions for individual instances. The presented approach is general and can be used with all classification models that output probabilities...
Marko Robnik-Sikonja, Igor Kononenko
IDEAL
2005
Springer
15 years 9 months ago
Exploiting Sequence Dependencies in the Prediction of Peroxisomal Proteins
Prediction of peroxisomal matrix proteins generally depends on the presence of one of two distinct motifs at the end of the amino acid sequence. PTS1 peroxisomal proteins have a we...
Mark Wakabayashi, John Hawkins, Stefan Maetschke, ...