1. PRN combined feature selection algorithm is proposed.

2. The WAS generally includes the Web page purification, feature selection, vector representation, training algorithm, classification al.

3. Feature selection tended to favor necessary improvements over desirable improvements.

4. The result of the feature selection in unsupervised learning is not as satisfactory as that in supervised learning.

5. Now there are several feature selection methods applied to gene expression data, such as Sequential Forward Selection, GA, S2N and so on.

6. And a no - search combined feature selection method based on KNN is proposed.

7. In particular methods for confidence estimation and feature selection with Support Vector Machines will be described.

8. Semi - automatic Feature Selection for Fine - Grained Image Retrieval. National Taiwan University, Taipei , Taiwan.

9. The key operation of data preprocessing is feature selection and extraction.

10. On feature selection, document frequency was combined with mutual information, and performance was improved.

11. In the feature selection stage, the computing approximate gains in parallel is adopted in order to solve the computational expensiveness of the model and system spending.

12. In particular, feature selection removes irrelevant features, increases efficiency of learning tasks, improves learning performance, and enhances comprehensibility of learned results.

13. Feature selection is the key part in image matching based on features.

14. Feature selection is essentially a satisfactory optimization problem in engineering applications.

15. In this paper, a web page classification with feature selection and fuzzy learning is proposed.

16. A new feature selection algorithm is presented using SVM, and then it is integrated into the classification procedure of polarimetric SAR images to construct a novel SVM-based classification method.

17. Feature selection from the keyboard or'teach and learn'from the hand - box .

18. In this paper, we proposed a new feature selection method called Conditional Mutual Information Maximin (CMIM). It can select a set of individually discriminating and weakly dependent features.

19. An improved immune particle swarm optimization was presented in this study in order to increase the effectiveness of feature selection for emotion recognition based on Galvanic Skin Response (GSR).

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20. The uncertainty coefficient is an information measure for a feature subset, and it is monotonic, so it can be taken as the feature selection measure.

21. The author gains insights from attribute reduction based on discernability matrix and proposes a few rough-set based text feature selection algorithms, i. e. , DB1, DB2 and LDB.

22. This thesis designs 4 main algorithms on the base of Feature Selection algorithm Framework.

23. Actually the kernel design in the recognition method based on discrete time - frequency representation is a problem of feature selection from the ambiguity functions to reduce feature dimension.

24. In order to allow users facilitate the operation of vector layers and raster layers, layer processing is implemented with feature selection set method.

25. Different features have different contribution to saliency, so a new approach for salient region detection with automatic feature selection and weighting is proposed.