Semi-supervised Learning by Entropy Minimization
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Summary
This framework, which motivates minimum entropy regularization, enables to incorporate unlabeled data in the standard supervised learning, and includes other approaches to the semi-supervised problem as particular or limiting cases.
- Type
- article
- Published
- 2004-12-01
- Cited by
- 2,537
- References
- 25
- OpenAlex
- https://openalex.org/W2145494108
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:7890982
Keywords
Semi-supervised learning, Machine learning, Computer science, Artificial intelligence, Entropy (arrow of time)
References
- Semi Supervised Logistic Regression
- Special Invited Paper-Additive logistic regression: A statistical view of boosting
- Statistical Decision Theory and Bayesian Analysis. Second Edition (James O. Berger)
- Statistical Decision Theory and Bayesian Analysis, Second Edition
- Analyzing the effectiveness and applicability of co-training
- A deterministic annealing approach to clustering
- Text Classification from Labeled and Unlabeled Documents using EM
- Expressive face recognition and synthesis
- Transductive Inference for Text Classification using Support Vector Machines
- Semi-Supervised Support Vector Machines
- Comprehensive database for facial expression analysis
- Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions
- Normal Discrimination with Unclassified Observations
- The relative value of labeled and unlabeled samples in pattern recognition with an unknown mixing parameter
- Learning with Multiple Labels
- Statistical learning theory
- Information Regularization with Partially Labeled Data
- Structure Learning in Conditional Probability Models via an Entropic Prior and Parameter Extinction
- Learning with Local and Global Consistency
- Restricted Bayes Optimal Classifiers
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- Instance-Level Constraint-Based Semisupervised Learning With Imposed Space-Partitioning
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- Graphical Models for Primarily Unsupervised Sequence Labeling
- Unsupervised Model Adaptation using Information-Theoretic Criterion
- Exploiting domain and task regularities for robust named entity recognition
- Semi-supervised Bio-named Entity Recognition with Word-Codebook Learning
- Prestructuring multilayer perceptrons based on information-theoretic modeling of a partido-alto-based grammar for afro-brazilian music: enhanced generalization and principles of parsimony, including an investigation of statistical paradigms
- Semi-Supervised Classification by Low Density Separation
- A Hybrid Generative/Discriminative Approach to Semi-Supervised Classifier Design
- Optimistic Active-Learning Using Mutual Information
- 3D Robotic Sensing of People: Human Perception, Representation and Activity Recognition
- Portable Traffic Data Processor
- Semi-Supervised Structured Output Learning Based on a Hybrid Generative and Discriminative Approach
- Semi-supervised training of Gaussian mixture models by conditional entropy minimization
- Predicting Linguistic Structure with Incomplete and Cross-Lingual Supervision
- Semi-supervised learning for acoustic and prosodic modeling in speech applications
- Machine learning approaches for dealing with limited bilingual data in statistical machine translation
- Learning with single view co-training and marginalized dropout
- Semi-supervised classification with privileged information
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