Introduction to pattern recognition and machine learning

"This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systematic account of its major topics such as pattern representation and nearest neighbour based classifiers, current topics -- neural networks, suppor...

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Bibliographic Details
Main Authors: Murty, M. Narasimha, (Author), Devi, V. Susheela, (Author)
Format: eBook
Language: English
Published: New Jersey : World Scientific, [2015]
Series: IISc lecture notes series ; 5.
Subjects:
Pattern recognition systems.
Machine learning.
elektronické knihy
electronic books
ISBN: 9789814335461
9814335460
9781680158588
1680158589
9789814335454
9814335452
Physical Description: 1 online resource

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Table of contents

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100 1 |a Murty, M. Narasimha,  |e author. 
245 1 0 |a Introduction to pattern recognition and machine learning /  |c M Narasimha Murty, Der V Susheela Devi (Indian Institute of Science, India). 
264 1 |a New Jersey :  |b World Scientific,  |c [2015] 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a IISc lecture notes series,  |x 2010-2402 ;  |v vol. 5 
504 |a Includes bibliographical references and index. 
505 0 |a Table of Contents; About the Authors; Preface; 1. Introduction; 1. Classifiers: An Introduction; 2. An Introduction to Clustering; 3. Machine Learning; Research Ideas; 2. Types of Data; 1. Features and Patterns; 2. Domain of a Variable; 3. Types of Features; 3.1. Nominal data; 3.1.1. Operations on nominal variables; 3.2. Ordinal data; 3.2.1. Operations possible on ordinal variables; 3.3. Interval-valued variables; 3.3.1. Operations possible on interval-valued variables; 3.4. Ratio variables; 3.5. Spatio-temporal data; 4. Proximity measures; 4.1. Fractional norms; 4.2. Are metrics essential? 
505 8 |a 4.3. Similarity between vectors4.4. Proximity between spatial patterns; 4.5. Proximity between temporal patterns; 4.6. Mean dissimilarity; 4.7. Peak dissimilarity; 4.8. Correlation coefficient; 4.9. Dynamic Time Warping (DTW) distance; 4.9.1. Lower bounding the DTW distance; Research Ideas; 3. Feature Extraction and Feature Selection; 1. Types of Feature Selection; 2. Mutual Information (MI) for Feature Selection; 3. Chi-square Statistic; 4. Goodman-Kruskal Measure; 5. Laplacian Score; 6. Singular Value Decomposition (SVD); 7. Non-negative Matrix Factorization (NMF). 
505 8 |a 8. Random Projections (RPs) for Feature Extraction8.1. Advantages of random projections; 9. Locality Sensitive Hashing (LSH); 10. Class Separability; 11. Genetic and Evolutionary Algorithms; 11.1. Hybrid GA for feature selection; 12. Ranking for Feature Selection; 12.1. Feature selection based on an optimization formulation; 12.2. Feature ranking using F-score; 12.3. Feature ranking using linear support vector machine (SVM) weight vector; 12.4. Ensemble feature ranking; 12.4.1. Using threshold-based feature selection techniques; 12.4.2. Evolutionary algorithm. 
505 8 |a 12.5. Feature ranking using number of label changes13. Feature Selection for Time Series Data; 13.1. Piecewise aggregate approximation; 13.2. Spectral decomposition; 13.3. Wavelet decomposition; 13.4. Singular Value Decomposition (SVD); 13.5. Common principal component loading based variable subset selection (CLeVer); Research Ideas; 4. Bayesian Learning; 1. Document Classification; 2. Naive Bayes Classifier; 3. Frequency-Based Estimation of Probabilities; 4. Posterior Probability; 5. Density Estimation; 6. Conjugate Priors; Research Ideas; 5. Classification. 
505 8 |a 1. Classification Without Learning2. Classification in High-Dimensional Spaces; 2.1. Fractional distance metrics; 2.2. Shrinkage-divergence proximity (SDP); 3. RandomForests; 3.1. Fuzzy random forests; 4. Linear Support Vector Machine (SVM); 4.1. SVM-kNN; 4.2. Adaptation of cutting plane algorithm; 4.3. Nystrom approximated SVM; 5. Logistic Regression; 6. Semi-supervised Classification; 6.1. Using clustering algorithms; 6.2. Using generative models; 6.3. Using low density separation; 6.4. Using graph-based methods; 6.5. Using co-training methods; 6.6. Using self-training methods. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a "This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systematic account of its major topics such as pattern representation and nearest neighbour based classifiers, current topics -- neural networks, support vector machines and decision trees -- attributed to the recent vast progress in this field are also dealt with. Introduction to Pattern Recognition and Machine Learning will equip readers, especially senior computer science undergraduates, with a deeper understanding of the subject matter."--  |c Provided by publisher 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Pattern recognition systems. 
650 0 |a Machine learning. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Devi, V. Susheela,  |e author. 
776 0 8 |i Print version:  |a Murty, M. Narasimha.  |t Introduction to pattern recognition and machine learning  |z 9789814335454  |w (DLC) 2014044796  |w (OCoLC)697260699 
830 0 |a IISc lecture notes series ;  |v 5.  |x 2010-2402 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpIPRML003/introduction-to-pattern?kpromoter=marc  |y Full text 

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