Computational Methods Feature Selection (16 results)

Hard Cover. Condition: Good. No Jacket. Ex-library with the usual features. The interior is clean and tight. Binding and cover are good. 419 pages. Ex-Library.

Hardcover. Condition: New. In shrink wrap. Looks like an interesting title.

Condition: New.

Condition: New. pp. 440.

Condition: New.

Condition: New. In English.

Condition: New.

Condition: New. Huan Liu, Hiroshi MotodaDue to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and.

Condition: gut. Computational Methods of Feature Selection (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) In englischer Sprache. pages.

Condition: As New. Unread book in perfect condition.

Hardcover. Condition: Brand New. 1st edition. 440 pages. 9.25x6.50x1.00 inches. In Stock.

Hardcover. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condition: As New. Unread book in perfect condition.

Buch. Condition: Neu. Neuware - Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and knowledge discovery. Highlighting current research issues, Computational Methods of Feature Selection introduces the basic concepts and principles, state-of-the-art algorithms, and novel applications of this tool.The book begins by exploring unsupervised, randomized, and causal feature selection. It then reports on some recent results of empowering feature selection, including active feature selection, decision-border estimate, the use of ensembles with independent probes, and incremental feature selection. This is followed by discussions of weighting and local methods, such as the ReliefF family, k-means clustering, local feature relevance, and a new interpretation of Relief. The book subsequently covers text classification, a new feature selection score, and both constraint-guided and aggressive feature selection. The final section examines applications of feature selection in bioinformatics, including feature construction as well as redundancy-, ensemble-, and penalty-based feature selection. Through a clear, concise, and coherent presentation of topics, this volume systematically covers the key concepts, underlying principles, and inventive applications of feature selection, illustrating how this powerful tool can efficiently harness massive, high-dimensional data and turn it into valuable, reliable information.…

Condition: New. Print on Demand pp. 440 This item is printed on demand.

Condition: New. PRINT ON DEMAND pp. 440.