Results 231 to 240 of about 717,161 (269)

Structured Feature Selection

2015 IEEE International Conference on Computer Vision (ICCV), 2015
Feature dimensionality reduction has been widely used in various computer vision tasks. We explore feature selection as the dimensionality reduction technique and propose to use a structured approach, based on the Markov Blanket (MB), to select features. We first introduce a new MB discovery algorithm, Simultaneous Markov Blanket (STMB) discovery, that
Tian Gao, Ziheng Wang 0001, Qiang Ji
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Incremental Feature Selection

Applied Intelligence, 1998
Feature selection is a problem of finding relevant features. When the number of features of a dataset is large and its number of patterns is huge, an effective method of feature selection can help in dimensionality reduction. An incremental probabilistic algorithm is designed and implemented as an alternative to the exhaustive and heuristic approaches.
Liu, H., Setiono, R.
openaire   +1 more source

Infinite Feature Selection

2015 IEEE International Conference on Computer Vision (ICCV), 2015
Filter-based feature selection has become crucial in many classification settings, especially object recognition, recently faced with feature learning strategies that originate thousands of cues. In this paper, we propose a feature selection method exploiting the convergence properties of power series of matrices, and introducing the concept of ...
ROFFO, GIORGIO, MELZI, SIMONE, CRISTANI, Marco   +2 more
openaire   +3 more sources

Swarmed Feature Selection

33rd Applied Imagery Pattern Recognition Workshop (AIPR'04), 2005
Feature selection is an important part of pattern recognition, helping to overcome the curse of dimensionality problem with classifiers, among other systems. In this work, we introduce a feature selection method using particle swarm optimization. Experiments using data of others and hyperspectral remote sensed data are used to measure the performance ...
Hiram A. Firpi, Erik D. Goodman
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Feature Selection for Clustering

2000
Clustering is an important data mining task. Data mining often concerns large and high-dimensional data but unfortunately most of the clustering algorithms in the literature are sensitive to largeness or high-dimensionality or both. Different features affect clusters differently, some are important for clusters while others may hinder the clustering ...
Manoranjan Dash, Huan Liu 0001
openaire   +1 more source

Feature Selection for Propositionalization

2002
Following the success of inductive logic programming on structurally complex but small problems, recently there has been strong interest in relational methods that scale to real-world databases. Propositionalization has already been shown to be a particularly promising approach for robustly and effectively handling larger relational data sets. However,
Mark-A. Krogel, Stefan Wrobel
openaire   +1 more source

Importance Degree of Features and Feature Selection

2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009
A novel measure, importance degree of features, is proposed to rank the features. And a new filter method is presented to carry out feature selection based on such measure. The monotonic property of this proposed measure can reduce the search space, which results in enhancing learning efficiency.
Di Xiao, Junfeng Zhang
openaire   +1 more source

Feature Selection for SVMs.

2000
We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard feature selection algorithms on both toy data and real-
Poggio, Tomaso A., Weston, Jason, Mukherjee, Sayan, Pontil, Massimiliano, Chapelle, Olivier, Vapnik, Vladimir   +5 more
openaire   +3 more sources

Boosting Feature Selection

2005
It is possible to reduce the error rate of a single classifier using a classifier ensemble. However, any gain in performance is undermined by the increased computation of performing classification several times. Here the AdaboostFS algorithm is proposed which builds on two popular areas of ensemble research: Adaboost and Ensemble Feature Selection (EFS)
D. B. Redpath, Katia Lebart
openaire   +1 more source

Regularization and feature selection for networked features

Proceedings of the 19th ACM international conference on Information and knowledge management, 2010
In the standard formalization of supervised learning problems, a datum is represented as a vector of features without prior knowledge about relationships among features. However, for many real world problems, we have such prior knowledge about structure relationships among features. For instance, in Microarray analysis where the genes are features, the
Hongliang Fei, Brian Quanz, Jun Huan
openaire   +1 more source