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AUSDM
2006
Springer
177views Data Mining» more  AUSDM 2006»
15 years 5 months ago
On The Optimal Working Set Size in Serial and Parallel Support Vector Machine Learning With The Decomposition Algorithm
The support vector machine (SVM) is a wellestablished and accurate supervised learning method for the classification of data in various application fields. The statistical learnin...
Tatjana Eitrich, Bruno Lang
87
Voted
CVPR
2006
IEEE
16 years 3 months ago
Meta-Evaluation of Image Segmentation Using Machine Learning
Image segmentation is a fundamental step in many computer vision applications. Generally, the choice of a segmentation algorithm, or parameterization of a given algorithm, is sele...
Hui Zhang, Sharath R. Cholleti, Sally A. Goldman, ...
ICMLA
2008
15 years 3 months ago
Ensemble Machine Methods for DNA Binding
We introduce three ensemble machine learning methods for analysis of biological DNA binding by transcription factors (TFs). The goal is to identify both TF target genes and their ...
Yue Fan, Mark A. Kon, Charles DeLisi
109
Voted
ICMLA
2009
14 years 11 months ago
An Ordinal Data Method for the Classification with Reject Option
In this work we consider the problem of binary classification where the classifier may abstain instead of classifying each observation, leaving the critical items for human evaluat...
Ricardo Sousa, Beatriz Mora, Jaime S. Cardoso
107
Voted
ICML
2003
IEEE
16 years 2 months ago
The Cross Entropy Method for Fast Policy Search
We present a learning framework for Markovian decision processes that is based on optimization in the policy space. Instead of using relatively slow gradient-based optimization al...
Shie Mannor, Reuven Y. Rubinstein, Yohai Gat