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Scalable Pattern Recognition Algorithms Applications in Computational Biology and Bioinformatics

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

04.04.2014

Abbildungen

XXII, 304 p. 55 illus., 10 illus. in color.

Verlag

Springer

Seitenzahl

304

Maße (L/B/H)

24,1/16/2,4 cm

Gewicht

658 g

Auflage

2014

Sprache

Englisch

ISBN

978-3-319-05629-6

Beschreibung

Rezension

From the book reviews:

“This book provides unique insights into how various soft computing and machine learning methods can be formulated and used in building efficient pattern recognition models. … This is a great resource to students and researchers in the fields of computer science, electrical and biomedical engineering. The author has explained the complex ideas through numerous examples which make conceptualization easy. … The well-organized chapters as well as use of different notations and typescripts make it a user-friendly reference.” (Parthiv Amin, Doody’s Book Reviews, August, 2014)

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

04.04.2014

Abbildungen

XXII, 304 p. 55 illus., 10 illus. in color.

Verlag

Springer

Seitenzahl

304

Maße (L/B/H)

24,1/16/2,4 cm

Gewicht

658 g

Auflage

2014

Sprache

Englisch

ISBN

978-3-319-05629-6

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Scalable Pattern Recognition Algorithms
  • Produktbild: Scalable Pattern Recognition Algorithms

  • Introduction to Pattern Recognition and Bioinformatics.-
    Part I Classification.-
    Neural Network Tree for Identification of Splice Junction and Protein Coding Region in DNA.- Design of String Kernel to Predict Protein Functional Sites Using Kernel-Based Classifiers.-
    Part II Feature Selection.-
    Rough Sets for Selection of Molecular Descriptors to Predict Biological Activity of Molecules.-
    f
    -Information Measures for Selection of Discriminative Genes from Microarray Data.- Identification of Disease Genes Using Gene Expression and Protein-Protein Interaction Data.- Rough Sets for Insilico Identification of Differentially Expressed miRNAs.-
    Part III Clustering.-
    Grouping Functionally Similar Genes from Microarray Data Using Rough-Fuzzy Clustering.- Mutual Information Based Supervised Attribute Clustering for Microarray Sample Classification.- Possibilistic Biclustering for Discovering Value-Coherent Overlapping d -Biclusters.- Fuzzy Measures and Weighted Co-Occurrence Matrix for Segmentation of Brain MR Images.