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IPMU
2010
Springer
13 years 7 months ago
Attribute Value Selection Considering the Minimum Description Length Approach and Feature Granularity
Abstract. In this paper we introduce a new approach to automatic attribute and granularity selection for building optimum regression trees. The method is based on the minimum descr...
Kemal Ince, Frank Klawonn
IEEEIAS
2009
IEEE
13 years 6 months ago
A Study on the Algorithm Based on Image Color Correlation Mining
: Because of the semantic gap between low-level feature and high-level semantic feature of images, the results of the traditional color-based image retrieval can't meet users&...
Yongyue Chen, Huosong Xia
INTERSPEECH
2010
13 years 3 months ago
Semi-supervised extractive speech summarization via co-training algorithm
Supervised methods for extractive speech summarization require a large training set. Summary annotation is often expensive and time consuming. In this paper, we exploit semisuperv...
Shasha Xie, Hui Lin, Yang Liu
PERCOM
2010
ACM
13 years 7 months ago
All for one or one for all? Combining heterogeneous features for activity spotting
Abstract—Choosing the right feature for motion based activity spotting is not a trivial task. Often, features derived by intuition or that proved to work well in previous work ar...
Ulf Blanke, Bernt Schiele, Matthias Kreil, Paul Lu...
JMLR
2010
151views more  JMLR 2010»
13 years 3 months ago
The Feature Selection Path in Kernel Methods
The problem of automatic feature selection/weighting in kernel methods is examined. We work on a formulation that optimizes both the weights of features and the parameters of the ...
Fuxin Li, Cristian Sminchisescu