Introduction to pattern recognition and machine learning:
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Singapore [u.a.]
World Scientific, IISc Press
2015
|
| Schriftenreihe: | IISc lecture notes series
5 |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027444627&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XVII, 383 S |
| ISBN: | 9789814335454 |
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Datensatz im Suchindex
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| adam_text | Titel: Introduction to pattern recognition and machine learning
Autor: Narasimha Murty, M
Jahr: 2015
Table of Contents About the Authors xiii Preface xv 1. Introduction 1 1. Classifiers: An Introduction.............. 5 2. An Introduction to Clustering............. 14 3. Machine Learning ................... 25 2. Types of Data 37 1. Features and Patterns................. 37 2. Domain of a Variable ................. 39 3. Types of Features ................... 41 3.1. Nominal data.................. 41 3.2. Ordinal data................... 45 3.3. Interval-valued variables ............ 48 3.4. Ratio variables.................. 49 3.5. Spatio-temporal data.............. 49 4. Proximity measures .................. 50 4.1. Fractional norms ................ 56 4.2. Are metrics essential?.............. 57 4.3. Similarity between vectors........... 59 4.4. Proximity between spatial patterns...... 61 4.5. Proximity between temporal patterns..... 62 vii
Table of Contents viii 4.6. Mean dissimilarity................ 63 4.7. Peak dissimilarity................ 63 4.8. Correlation coefficient.............. 64 4.9. Dynamic Time Warping (DTW) distance ... 64 3. Feature Extraction and Feature Selection 75 1. Types of Feature Selection............... 76 2. Mutual Information (MI) for Feature Selection ... 78 3. Chi-square Statistic .................. 79 4. Goodman-Kruskal Measure.............. 81 5. Laplacian Score..................... 81 6. Singular Value Decomposition (SVD) ........ 83 7. Non-negative Matrix Factorization (NMF)...... 84 8. Random Projections (RPs) for Feature Extraction ....................... 86 8.1. Advantages of random projections....... 88 9. Locality Sensitive Hashing (LSH)........... 88 10. Class Separability ................... 90 11. Genetic and Evolutionary Algorithms........ 91 11.1. Hybrid GA for feature selection........ 92 12. Ranking for Feature Selection............. 96 12.1. Feature selection based on an optimization formulation.................... 97 12.2. Feature ranking using F-score......... 99 12.3. Feature ranking using linear support vector machine (SVM) weight vector......... 100 12.4. Ensemble feature ranking............ 101 12.5. Feature ranking using number of label changes................. 103 13. Feature Selection for Time Series Data........ 103 13.1. Piecewise aggregate approximation...... 103 13.2. Spectral decomposition............. 104 13.3. Wavelet decomposition............. 104 13.4. Singular Value Decomposition (SVD)..... 104 13.5. Common principal component loading based variable subset selection (CLeVer)....... 104
Table of Contents IX 4. Bayesian Learning 111 1. Document Classification................ Ill 2. Naive Bayes Classifier................. 113 3. Frequency-Based Estimation of Probabilities .... 115 4. Posterior Probability.................. 117 5. Density Estimation................... 119 6. Conjugate Priors.................... 126 5. Classification 135 1. Classification Without Learning ........... 135 2. Classification in High-Dimensional Spaces...... 139 2.1. Fractional distance metrics........... 141 2.2. Shrinkage-divergence proximity (SDP) .... 143 3. Random Forests .................... 144 3.1. Fuzzy random forests.............. 148 4. Linear Support Vector Machine (SVM) ....... 150 4.1. SVM-kNN.................... 153 4.2. Adaptation of cutting plane algorithm .... 154 4.3. Nystrom approximated SVM........... 155 5. Logistic Regression................... 156 6. Semi-supervised Classification............. 159 6.1. Using clustering algorithms........... 160 6.2. Using generative models ............ 160 6.3. Using low density separation.......... 161 6.4. Using graph-based methods .......... 162 6.5. Using co-training methods........... 164 6.6. Using self-training methods........... 165 6.7. SVM for semi-supervised classification .... 166 6.8. Random forests for semi-supervised classification................... 166 7. Classification of Time-Series Data .......... 167 7.1. Distance-based classification.......... 168 7.2. Feature-based classification........... 169 7.3. Model-based classification ........... 170
x Table of Contents 6. Classification using Soft Computing Techniques 177 1. Introduction ...................... 177 2. Fuzzy Classification .................. 178 2.1. Fuzzy fc-nearest neighbor algorithm...... 179 3. Rough Classification.................. 179 3.1. Rough set attribute reduction......... 180 3.2. Generating decision rules............ 181 4. GAs........................... 182 4.1. Weighting of attributes using G A....... 182 4.2. Binary pattern classification using GA .... 184 4.3. Rule-based classification using GAs...... 185 4.4. Time series classification............ 187 4.5. Using generalized Choquet integral with signed fuzzy measure for classification using GAs.................... 187 4.6. Decision tree induction using Evolutionary algorithms ............ 191 5. Neural Networks for Classification.......... 195 5.1. Multi-layer feed forward network with backpropagation.............. 197 5.2. Training a feedforward neural network using GAs.................... 199 6. Multi-label Classification ............... 202 6.1. Multi-label kNN (mL-kNN) .......... 203 6.2. Probabilistic classifier chains (PCC)...... 204 6.3. Binary relevance (BR) ............. 205 6.4. Using label powersets (LP)........... 205 6.5. Neural networks for Multi-label classification................... 206 6.6. Evaluation of multi-label classification .... 209 7. Data Clustering 215 1. Number of Partitions ................. 215 2. Clustering Algorithms................. 218 2.1. R-means algorithm............... 219
Table of Contents xi 2.2. Leader algorithm ................ 223 2.3. BIRCH: Balanced Iterative Reducing and Clustering using Hierarchies........ 225 2.4. Clustering based on graphs........... 230 3. Why Clustering?.................... 241 3.1. Data compression................ 241 3.2. Outlier detection ................ 242 3.3. Pattern synthesis................ 243 4. Clustering Labeled Data................ 246 4.1. Clustering for classification........... 246 4.2. Knowledge-based clustering .......... 250 5. Combination of Clusterings.............. 255 8. Soft Clustering 263 1. Soft Clustering Paradigms............... 264 2. Fuzzy Clustering.................... 266 2.1. Fuzzy iC-means algorithm ........... 267 3. Rough Clustering.................... 269 3.1. Rough A-means algorithm........... 271 4. Clustering Based on Evolutionary Algorithms .... 272 5. Clustering Based on Neural Networks........ 281 6. Statistical Clustering.................. 282 6.1. OKM algorithm................. 283 6.2. EM-based clustering............... 285 7. Topic Models...................... 293 7.1. Matrix factorization-based methods...... 295 7.2. Divide-and-conquer approach ......... 296 7.3. Latent Semantic Analysis (LSA)........ 299 7.4. SYD and PCA.................. 302 7.5. Probabilistic Latent Semantic Analysis (PLSA)...................... 307 7.6. Non-negative Matrix Factorization (NMF)...................... 310 7.7. LDA ....................... 311 7.8. Concept and topic................ 316
Table of Contents xii 9. Application — Social and Information Networks 321 1. Introduction ...................... 321 2. Patterns in Graphs................... 322 3. Identification of Communities in Networks...... 326 3.1. Graph partitioning............... 328 3.2. Spectral clustering................ 329 3.3. Linkage-based clustering............ 331 3.4. Hierarchical clustering ............. 331 3.5. Modularity optimization for partitioning graphs...................... 333 4. Link Prediction..................... 340 4.1. Proximity functions............... 341 5. Information Diffusion ................. 347 5.1. Graph-based approaches............ 348 5.2. Non-graph approaches ............. 349 6. Identifying Specific Nodes in a Social Network . . . 353 7. Topic Models...................... 355 7.1. Probabilistic latent semantic analysis (pLSA)...................... 355 7.2. Latent dirichlet allocation (LDA)....... 357 7.3. Author-topic model............... 359 Index 365
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| id | DE-604.BV042002627 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T16:59:51Z |
| institution | BVB |
| isbn | 9789814335454 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027444627 |
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| spellingShingle | Narasimha Murty, M. Susheela Devi, Der V. Introduction to pattern recognition and machine learning IISc lecture notes series Maschinelles Lernen (DE-588)4193754-5 gnd Mustererkennung (DE-588)4040936-3 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4040936-3 |
| title | Introduction to pattern recognition and machine learning |
| title_auth | Introduction to pattern recognition and machine learning |
| title_exact_search | Introduction to pattern recognition and machine learning |
| title_full | Introduction to pattern recognition and machine learning M. Narasimha Murty ; Der V. Susheela Devi |
| title_fullStr | Introduction to pattern recognition and machine learning M. Narasimha Murty ; Der V. Susheela Devi |
| title_full_unstemmed | Introduction to pattern recognition and machine learning M. Narasimha Murty ; Der V. Susheela Devi |
| title_short | Introduction to pattern recognition and machine learning |
| title_sort | introduction to pattern recognition and machine learning |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Mustererkennung (DE-588)4040936-3 gnd |
| topic_facet | Maschinelles Lernen Mustererkennung |
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