Supervised Learning: Active, Ensemble, Rare-Class and Online | |
Time-Evolving Relational Classification and Ensemble Methods | p. 1 |
Active Learning for Hierarchical Text Classification | p. 14 |
TeamSkill Evolved: Mixed Classification Schemes for Team-Based Multi-player Games | p. 26 |
A Novel Weighted Ensemble Technique for Time Series Forecasting | p. 38 |
Techniques for Efficient Learning without Search | p. 50 |
An Aggressive Margin-Based Algorithm for Incremental Learning | p. 62 |
Two-View Online Learning | p. 74 |
A Generic Classifier-Ensemble Approach for Biomedical Named Entity Recognition | p. 86 |
Neighborhood Random Classification | p. 98 |
SRF: A Framework for the Study of Classifier Behavior under Training Set Mislabeling Noise | p. 109 |
Building Decision Trees for the Multi-class Imbalance Problem | p. 122 |
Scalable Random Forests for Massive Data | p. 135 |
Hybrid Random Forests: Advantages of Mixed Trees in Classifying Text Data | p. 147 |
Learning Tree Structure of Label Dependency for Multi-label Learning | p. 159 |
Multiple Instance Learning for Group Record Linkage | p. 171 |
Incremental Set Recommendation Based on Class Differences | p. 183 |
Active Learning for Cross Language Text Categorization | p. 195 |
Evasion Attack of Multi-class Linear Classifiers | p. 207 |
Foundation of Mining Class-Imbalanced Data | p. 219 |
Active Learning with c-Certainty | p. 231 |
A Term Association Translation Model for Naive Bayes Text Classification | p. 243 |
A Double-Ensemble Approach for Classifying Skewed Data Streams | p. 254 |
Generating Balanced Classifier-Independent Training Samples from Unlabeled Data | p. 266 |
Nyström Approximate Model Selection for LSSVM | p. 282 |
Exploiting Label Dependency for Hierarchical Multi-label Classification | p. 294 |
Diversity Analysis on Boosting Nominal Concepts | p. 306 |
Extreme Value Prediction for Zero-Inflated Data | p. 318 |
Learning to Diversify Expert Finding with Subtopics | p. 330 |
An Associative Classifier for Uncertain Datasets | p. 342 |
Unsupervised Learning: Clustering, Probabilistic Modeling | |
Neighborhood-Based Smoothing of External Cluster Validity Measures | p. 354 |
Sequential Entity Group Topic Model for Getting Topic Flows of Entity Groups within One Document | p. 366 |
Topological Comparisons of Proximity Measures | p. 379 |
Quad-tuple PLSA: Incorporating Entity and Its Rating in Aspect Identification | p. 392 |
Clustering-Based ¿-Anonymity | p. 405 |
Unsupervised Ensemble Learning for Mining Top-n Outliers | p. 418 |
Towards Personalized Context-Aware Recommendation by Mining Context Logs through Topic Models | p. 431 |
Mining of Temporal Coherent Subspace Clusters in Multivariate Time Series Databases | p. 444 |
A Vertex Similarity Probability Model for Finding Network Community Structure | p. 456 |
Hybrid-¿-greedy for Mobile Context-Aware Recommender System | p. 468 |
Unsupervised Multi-label Text Classification Using a World Knowledge Ontology | p. 480 |
Semantic Social Network Analysis with Text Corpora | p. 493 |
Visualizing Clusters in Parallel Coordinates for Visual Knowledge Discovery | p. 505 |
Feature Enriched Nonparametric Bayesian Co-clustering | p. 517 |
Shape-Based Clustering for Time Series Data | p. 530 |
Privacy-Preserving EM Algorithm for Clustering on Social Network | p. 542 |
Named Entity Recognition and Identification for Finding the Owner of a Home Page | p. 554 |
Clustering and Understanding Documents via Discrimination Information Maximization | p. 566 |
A Semi-supervised Incremental Clustering Algorithm for Streaming Data | p. 578 |
Unsupervised Sparse Matrix Co-clustering for Marketing and Sales Intelligence | p. 591 |
Expectation-Maximization Collaborative Filtering with Explicit and Implicit Feedback | p. 604 |
Author Index | p. 617 |
Table of Contents provided by Ingram. All Rights Reserved. |
The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.