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Machine Learning and Knowledge Discovery in Databases

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Cover of 'Machine Learning and Knowledge Discovery in Databases'

Table of Contents

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    Book Overview
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    Chapter 1 Incremental Local Evolutionary Outlier Detection for Dynamic Social Networks
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    Chapter 2 How Long Will She Call Me? Distribution, Social Theory and Duration Prediction
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    Chapter 3 Discovering Nested Communities
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    Chapter 4 CSI: Community-Level Social Influence Analysis
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    Chapter 5 Supervised Learning of Syntactic Contexts for Uncovering Definitions and Extracting Hypernym Relations in Text Databases
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    Chapter 6 Error Prediction with Partial Feedback
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    Chapter 7 Boot-Strapping Language Identifiers for Short Colloquial Postings
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    Chapter 8 A Pairwise Label Ranking Method with Imprecise Scores and Partial Predictions
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    Chapter 9 Learning Socially Optimal Information Systems from Egoistic Users
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    Chapter 10 Socially Enabled Preference Learning from Implicit Feedback Data
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    Chapter 11 Cross-Domain Recommendation via Cluster-Level Latent Factor Model
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    Chapter 12 Minimal Shrinkage for Noisy Data Recovery Using Schatten- p Norm Objective
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    Chapter 13 Noisy Matrix Completion Using Alternating Minimization
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    Chapter 14 A Nearly Unbiased Matrix Completion Approach
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    Chapter 15 A Counterexample for the Validity of Using Nuclear Norm as a Convex Surrogate of Rank
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    Chapter 16 Efficient Rank-one Residue Approximation Method for Graph Regularized Non-negative Matrix Factorization
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    Chapter 17 Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data
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    Chapter 18 An Analysis of Tensor Models for Learning on Structured Data
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    Chapter 19 Learning Modewise Independent Components from Tensor Data Using Multilinear Mixing Model
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    Chapter 20 Taxonomic Prediction with Tree-Structured Covariances
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    Chapter 21 Position Preserving Multi-Output Prediction
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    Chapter 22 Structured Output Learning with Candidate Labels for Local Parts
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    Chapter 23 Shared Structure Learning for Multiple Tasks with Multiple Views
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    Chapter 24 Using Both Latent and Supervised Shared Topics for Multitask Learning
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    Chapter 25 Probabilistic Clustering for Hierarchical Multi-Label Classification of Protein Functions
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    Chapter 26 Multi-core Structural SVM Training
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    Chapter 27 Multi-label Classification with Output Kernels
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    Chapter 28 Boosting for Unsupervised Domain Adaptation
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    Chapter 29 Automatically Mapped Transfer between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines
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    Chapter 30 A Layered Dirichlet Process for Hierarchical Segmentation of Sequential Grouped Data
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    Chapter 31 A Bayesian Classifier for Learning from Tensorial Data
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    Chapter 32 Prediction with Model-Based Neutrality
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    Chapter 33 Decision-Theoretic Sparsification for Gaussian Process Preference Learning
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    Chapter 34 Variational Hidden Conditional Random Fields with Coupled Dirichlet Process Mixtures
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    Chapter 35 Sparsity in Bayesian Blind Source Separation and Deconvolution
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    Chapter 36 Nested Hierarchical Dirichlet Process for Nonparametric Entity-Topic Analysis
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    Chapter 37 Knowledge Intensive Learning: Combining Qualitative Constraints with Causal Independence for Parameter Learning in Probabilistic Models
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    Chapter 38 Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
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    Chapter 39 Machine Learning and Knowledge Discovery in Databases
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    Chapter 40 From Topic Models to Semi-supervised Learning: Biasing Mixed-Membership Models to Exploit Topic-Indicative Features in Entity Clustering
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    Chapter 41 Hub Co-occurrence Modeling for Robust High-Dimensional k NN Classification
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    Chapter 42 Fast k NN Graph Construction with Locality Sensitive Hashing
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    Chapter 43 Mixtures of Large Margin Nearest Neighbor Classifiers
Attention for Chapter 5: Supervised Learning of Syntactic Contexts for Uncovering Definitions and Extracting Hypernym Relations in Text Databases
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Chapter title
Supervised Learning of Syntactic Contexts for Uncovering Definitions and Extracting Hypernym Relations in Text Databases
Chapter number 5
Book title
Machine Learning and Knowledge Discovery in Databases
Published by
Springer, Berlin, Heidelberg, September 2013
DOI 10.1007/978-3-642-40991-2_5
Book ISBNs
978-3-64-240990-5, 978-3-64-240991-2
Authors

Guido Boella, Luigi Di Caro

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 11%
Unknown 8 89%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 44%
Researcher 2 22%
Student > Ph. D. Student 1 11%
Other 1 11%
Student > Postgraduate 1 11%
Other 0 0%
Readers by discipline Count As %
Computer Science 6 67%
Linguistics 1 11%
Engineering 1 11%
Unknown 1 11%