Machine Learning and Knowledge Discovery in Databases

Table of Contents

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   Book Overview
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   Chapter 1 Machine Learning for Robotics
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   Chapter 2 Declarative Modeling for Machine Learning and Data Mining
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   Chapter 3 Machine Learning Methods for Music Discovery and Recommendation
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   Chapter 4 Solving Problems with Visual Analytics: Challenges and Applications
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   Chapter 5 Analyzing Text and Social Network Data with Probabilistic Models
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   Chapter 6 Discovering Descriptive Tile Trees
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   Chapter 7 Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees
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   Chapter 8 Smoothing Categorical Data
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    Chapter 9 An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning
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    Chapter 10 Machine Learning and Knowledge Discovery in Databases
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    Chapter 11 Combining Subjective Probabilities and Data in Training Markov Logic Networks
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    Chapter 12 Score-Based Bayesian Skill Learning
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    Chapter 13 A Note on Extending Generalization Bounds for Binary Large-Margin Classifiers to Multiple Classes
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    Chapter 14 Extension of the Rocchio Classification Method to Multi-modal Categorization of Documents in Social Media
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    Chapter 15 Label-Noise Robust Logistic Regression and Its Applications
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    Chapter 16 Sentiment Classification with Supervised Sequence Embedding
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    Chapter 17 The Bitvector Machine: A Fast and Robust Machine Learning Algorithm for Non-linear Problems
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    Chapter 18 Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methods
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    Chapter 19 Hypergraph Spectra for Semi-supervised Feature Selection
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    Chapter 20 Learning Neighborhoods for Metric Learning
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    Chapter 21 Massively Parallel Feature Selection: An Approach Based on Variance Preservation
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    Chapter 22 PCA, Eigenvector Localization and Clustering for Side-Channel Attacks on Cryptographic Hardware Devices
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    Chapter 23 Classifying Stem Cell Differentiation Images by Information Distance
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    Chapter 24 Machine Learning and Knowledge Discovery in Databases
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    Chapter 25 Geodesic Analysis on the Gaussian RKHS Hypersphere
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    Chapter 26 Boosting Nearest Neighbors for the Efficient Estimation of Posteriors
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    Chapter 27 Diversity Regularized Ensemble Pruning
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    Chapter 28 Ensembles on Random Patches
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    Chapter 29 An Efficiently Computable Support Measure for Frequent Subgraph Pattern Mining
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    Chapter 30 Efficient Graph Kernels by Randomization
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    Chapter 31 Graph Mining for Object Tracking in Videos
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    Chapter 32 Hypergraph Learning with Hyperedge Expansion
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    Chapter 33 Nearly Exact Mining of Frequent Trees in Large Networks
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    Chapter 34 Reachability Analysis and Modeling of Dynamic Event Networks
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    Chapter 35 CC-MR – Finding Connected Components in Huge Graphs with MapReduce
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    Chapter 36 Fast Near Neighbor Search in High-Dimensional Binary Data
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    Chapter 37 Fully Sparse Topic Models
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    Chapter 38 Learning Compact Class Codes for Fast Inference in Large Multi Class Classification
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    Chapter 39 ParCube: Sparse Parallelizable Tensor Decompositions
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    Chapter 40 Stochastic Coordinate Descent Methods for Regularized Smooth and Nonsmooth Losses
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    Chapter 41 Sublinear Algorithms for Penalized Logistic Regression in Massive Datasets
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    Chapter 42 Author Name Disambiguation Using a New Categorical Distribution Similarity
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    Chapter 43 Lifted Online Training of Relational Models with Stochastic Gradient Methods
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    Chapter 44 Scalable Relation Prediction Exploiting Both Intrarelational Correlation and Contextual Information
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    Chapter 45 Machine Learning and Knowledge Discovery in Databases
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    Chapter 46 Efficient Training of Graph-Regularized Multitask SVMs
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    Chapter 47 Geometry Preserving Multi-task Metric Learning
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    Chapter 48 Learning and Inference in Probabilistic Classifier Chains with Beam Search
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    Chapter 49 Learning Multiple Tasks with Boosted Decision Trees
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    Chapter 50 Multi-Task Boosting by Exploiting Task Relationships
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    Chapter 51 Nonparametric Bayesian Mixed-effect Model: a Sparse Gaussian Process Approach
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    Chapter 52 Collective Information Extraction with Context-Specific Consistencies
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    Chapter 53 Supervised Learning of Semantic Relatedness
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    Chapter 54 Unsupervised Bayesian Part of Speech Inference with Particle Gibbs
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    Chapter 55 WikiSent: Weakly Supervised Sentiment Analysis through Extractive Summarization with Wikipedia
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    Chapter 56 Adaptive Two-View Online Learning for Math Topic Classification
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    Chapter 57 BDUOL: Double Updating Online Learning on a Fixed Budget
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    Chapter 58 Handling Time Changing Data with Adaptive Very Fast Decision Rules
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    Chapter 59 Improved Counter Based Algorithms for Frequent Pairs Mining in Transactional Data Streams
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    Chapter 60 Mirror Descent for Metric Learning: A Unified Approach