Semi-supervised learning by disagreement
Knowledge and Information SystemsPublished 18 May 2009
Zhi‐Hua Zhou, Ming Li
Citations416
SJR quartileQ2
SJR score0.83
SNIP1.37
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
An introduction to research advances in disagreement-based semi-supervised learning is provided, where multiple learners are trained for the task and the disagreements among the learners are exploited during the semi-supervised learning process.
Abstract
In many real-world tasks, there are abundant unlabeled examples but the number of labeled training examples is limited, because labeling the examples requires human efforts and expertise. So, semi-supervised learning which tries to exploit unlabeled examples to improve learning performance has become a hot topic. Disagreement-based semi-supervised learning is an interesting paradigm, where multiple learners are trained for the task and the disagreements among the learners are exploited during the semi-supervised learning process. This survey article provides an introduction to research advances in this paradigm.
Keywords
Computer Science
Machine LearningRandom Forests
126,192 Citations2001Leo Breiman
Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal of the Royal Statistical Society Series B (Statistical Methodology)Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
49,657 Citations1977A. P. Dempster, N. M. Laird +1 more
An Introduction to the Bootstrap
39,744 Citations1994Bradley Efron, Robert Tibshirani
TechnometricsStatistical Learning Theory
26,913 Citations1999Yuhai Wu, Vladimir Vapnik
Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
ScienceNonlinear Dimensionality Reduction by Locally Linear Embedding
15,035 Citations2000Sam T. Roweis, Lawrence K. Saul
Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Proceedings of the 24th international conference on Machine learning
11,727 Citations2007
This is an index to the papers that appear in the Proceedings of the 29th International Conference on Machine Learning (ICML-12).
Knowledge and Information SystemsTop 10 algorithms in data mining
5,685 Citations2007Xindong Wu, Vipin Kumar +12 more
This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
Combining labeled and unlabeled data with co-training
5,604 Citations1998Avrim Blum, Tom M. Mitchell
The MIT Press eBooksSemi-Supervised Learning
4,308 Citations2006Olivier Chapelle, Schölkopf, B. +1 more
This first comprehensive overview of semi-supervised learning presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.
Minds at UW (University of Wisconsin)Semi-Supervised Learning Literature Survey
3,868 Citations2005Xiaojin Zhu
The study clearly indicates that the common practice of stripwise precommercial thinning is unjustified, and the justification of heavy 'chessboard' thinning (with pruning) depends on whether the potential reduction in rotation length and the improvement in wood quality outweigh the discounted costs of pre-commercial thinning and selection and pruning of crop trees.
MPG.PuRe (Max Planck Society)Learning with Local and Global Consistency
3,750 Citations2003Dengyong Zhou, Olivier Bousquet +3 more
A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points.
Semi-supervised learning using Gaussian fields and harmonic functions
3,377 Citations2003Xiaojin Zhu, Zoubin Ghahramani +1 more
Neural ComputationCanonical Correlation Analysis: An Overview with Application to Learning Methods
3,304 Citations2004David R. Hardoon, Sándor Szedmák +1 more
A general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text and compares orthogonalization approaches against a standard cross-representation retrieval technique known as the generalized vector space model is presented.
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
3,266 Citations2006Mikhail Belkin, Partha Niyogi +1 more
A semi-supervised framework that incorporates labeled and unlabeled data in a general-purpose learner is proposed and properties of reproducing kernel Hilbert spaces are used to prove new Representer theorems that provide theoretical basis for the algorithms.
Machine LearningText Classification from Labeled and Unlabeled Documents using EM
2,749 Citations2000Kamal Nigam, Andrew Kachites McCallum +2 more
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents, and presents two extensions to the algorithm that improve classification accuracy under these conditions.
Transductive Inference for Text Classification using Support Vector Machines
2,717 Citations1999Thorsten Joachims
An analysis of why TSVMs are well suited for text classi(cid:12)cation is presented, and an algorithm for training TSVMs e(cid:14)-ciently, handling 10,000 examples and more is proposed.
Unsupervised word sense disambiguation rivaling supervised methods
2,408 Citations1995David Yarowsky
An unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require time-consuming hand annotations.
IEEE Transactions on Circuits and Systems for Video TechnologyRelevance feedback: a power tool for interactive content-based image retrieval
1,764 Citations1998Yong Rui, Thomas S. Huang +2 more
A relevance feedback based interactive retrieval approach that effectively takes into account the subjectivity of human perception of visual content and the gap between high-level concepts and low-level features in CBIR.
Query by committee
1,657 Citations1992H. Sebastian Seung, Manfred Opper +1 more
It is suggested that asymptotically finite information gain may be an important characteristic of good query algorithms, in which a committee of students is trained on the same data set.
IEEE Transactions on Knowledge and Data EngineeringTri-training: exploiting unlabeled data using three classifiers
1,185 Citations2005Zhi‐Hua Zhou, Ming Li
Experiments on UCI data sets and application to the Web page classification task indicate that tri-training can effectively exploit unlabeled data to enhance the learning performance.
A Sequential Algorithm for Training Text Classifiers
1,158 Citations1994David Lewis, William A. Gale
An algorithm for sequential sampling during machine learning of statistical classifiers was developed and tested on a newswire text categorization task and reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness.
Analyzing the effectiveness and applicability of co-training
1,028 Citations2000Kamal Nigam, Rayid Ghani
It is demonstrated that when learning from labeled and unlabeled data, algorithms explicitly leveraging a natural independent split of the features outperform algorithms that do not and may out-perform algorithms not using a split.
Semi-supervised Learning by Entropy Minimization.
1,021 Citations2005Yves Grandvalet, Yoshua Bengio
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.
Research Showcase @ Carnegie Mellon University (Carnegie Mellon University)Learning from Labeled and Unlabeled Data using Graph Mincuts
947 Citations2018Avrim Blum, Shuchi Chawla
An algorithm based on finding minimum cuts in graphs, that uses pairwise relationships among the examples in order to learn from both labeled and unlabeled data is considered.
Unsupervised Models for Named Entity Classification
817 Citations1999Michael Collins, Yoram Singer
It is shown that the use of unlabeled data can reduce the requirements for supervision to just 7 simple "seed" rules, gaining leverage from natural redundancy in the data.
IEEE Communications MagazinePattern classification using neural networks
812 Citations1989Richard P. Lippmann
The author extends a previous review and focuses on feed-forward neural-net classifiers for static patterns with continuous-valued inputs, examining probabilistic, hyperplane, kernel, and exemplar classifiers.
Machine LearningSemi-Supervised Learning on Riemannian Manifolds
807 Citations2004Mikhail Belkin, Partha Niyogi
An algorithmic framework to classify a partially labeled data set in a principled manner and models the manifold using the adjacency graph for the data and approximates the Laplace-Beltrami operator by the graph Laplacian.
Proceedings of the 24th annual meeting on Association for Computational Linguistics
799 Citations1986Alan W. Biermann
This volume is the official record of the Twenty-Fourth Annual Meeting of the Association for Computational Linguistics and contains abstracts for the tutorials and panels as well as the texts of the presented papers.
IEEE Transactions on Knowledge and Data EngineeringLabel Propagation through Linear Neighborhoods
713 Citations2007Fei Wang, Changshui Zhang
Semi-Supervised Classification by Low Density Separation
710 Citations2005Olivier Chapelle, Alexander Zien +1 more
Three semi-supervised algorithms are proposed: deriving graph-based distances that emphazise low density regions between clusters, followed by training a standard SVM, and optimizing the Transductive SVM objective function by gradient descent.
Learning dictionaries for information extraction by multi-level bootstrapping
688 Citations1999Ellen Riloff, Rosie Jones
A multilevel bootstrapping algorithm is presented that generates both the semantic lexicon and extraction patterns simultaneously simultaneously and produces high-quality dictionaries for several semantic categories.
Machine LearningLearning From Noisy Examples
667 Citations1988Dana Angluin, Philip Laird
This paper shows that when the teacher may make independent random errors in classifying the example data, the strategy of selecting the most consistent rule for the sample is sufficient, and usually requires a feasibly small number of examples, provided noise affects less than half the examples on average.
IEEE Transactions on Geoscience and Remote SensingThe effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon
572 Citations1994B.M. Shahshahani, D. A. Landgrebe
By using additional unlabeled samples that are available at no extra cost, the performance may be improved, and therefore the Hughes phenomenon can be mitigated and therefore more representative estimates can be obtained.
Hidden Markov support vector machines
469 Citations2003Yasemin Altün, Ioannis Tsochantaridis +1 more
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines and Hidden Markov Models which it is called HM-SVMs and handles dependencies between neighboring labels using Viterbi decoding.
Enhancing Supervised Learning with Unlabeled Data
459 Citations2000Sally A. Goldman, Yan Zhou
A new semi-supervised learning method called co-learning that is designed to use unlabeled data to enhance standard supervised learning algorithms to leverage off the fact that they have different representations of the hypotheses and are likely to detect different patterns in labeled data.
Beyond the point cloud
443 Citations2005Vikas Sindhwani, Partha Niyogi +1 more
This paper constructs a family of data-dependent norms on Reproducing Kernel Hilbert Spaces (RKHS) that allow the structure of the RKHS to reflect the underlying geometry of the data.
IEEE Transactions on Systems Man and Cybernetics - Part A Systems and HumansImprove Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
381 Citations2007Ming Li, Zhi‐Hua Zhou
Case studies on three medical data sets and a successful application to microcalcification detection for breast cancer diagnosis show that undiagnosed samples are helpful in building CAD systems, and Co-Forest is able to enhance the performance of the hypothesis that is learned on only a small amount of diagnosed samples by utilizing the available undiognosed samples.
The Concave-Convex Procedure (CCCP)
381 Citations2001Alan Yuille, Anand Rangarajan
This work introduces the Concave-Convex procedure (CCCP) which constructs discrete time iterative dynamical systems which are guaranteed to monotonically decrease global optimization/energy functions and proves relationships to some applications of Legendre transform techniques.
Trading convexity for scalability
361 Citations2006Ronan Collobert, Fabian H. Sinz +2 more
It is shown how concave-convex programming can be applied to produce faster SVMs where training errors are no longer support vectors, and much faster Transductive SVMs.
Brunel University Research Archive (BURA) (Brunel University London)Two view learning: SVM-2K, Theory and Practice
331 Citations2005Jason Farquhar, David R. Hardoon +3 more
On Manifold Regularization.
323 Citations2005Misha Belkin, Partha Niyogi +1 more
A semisupervised framework that incorporates labeled and unlabeled data in a generalpurpose learner and gives rise to a regularized form of spectral clustering with an out-of-sample extension is proposed.
Neural Information Processing SystemsA Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data
289 Citations1996David J. Miller, Hasan S. Uyar
A classifier structure and learning algorithm that make effective use of unlabelled data to improve performance and is a "mixture of experts" structure that is equivalent to the radial basis function (RBF) classifier, but unlike RBFs, is amenable to likelihood-based training.
The MIT Press eBooksPAC Generalization Bounds for Co-training
287 Citations2002Sanjoy Dasgupta, Michael L. Littman +1 more
A new PAC-style bound on generalization error is given which justifies both the use of confidences — partial rules and partial labeling of the unlabeled data — and theUse of an agreement-based objective function as suggested by Collins and Singer.
Semi-supervised learning using randomized mincuts
268 Citations2004Avrim Blum, John Lafferty +2 more
The experiments on several datasets show that when the structure of the graph supports small cuts, this can result in highly accurate classifiers with good accuracy/coverage tradeoffs, and can be given theoretical justification from both a Markov random field perspective and from sample complexity considerations.
Label propagation through linear neighborhoods
261 Citations2006Fei Wang, Changshui Zhang
A novel graph-based semi supervised learning approach is proposed based on a linear neighborhood model, which assumes that each data point can be linearly reconstructed from its neighborhood, and can propagate the labels from the labeled points to the whole data set using these linear neighborhoods with sufficient smoothness.
Research Showcase @ Carnegie Mellon University (Carnegie Mellon University)Co-Training and Expansion: Towards Bridging Theory and Practice
261 Citations2018Maria-Florina Balcan, Avrim Blum +1 more
A much weaker "expansion" assumption on the underlying data distribution is proposed, that is proved to be sufficient for iterative co-training to succeed given appropriately strong PAC-learning algorithms on each feature set, and that to some extent is necessary as well.
IEEE Transactions on Pattern Analysis and Machine IntelligenceSemisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction
252 Citations2004Ira L. Cohen, Fábio Gagliardi Cozman +3 more
A new analysis is provided that shows under what conditions unlabeled data can be used in learning to improve classification performance, and how the resulting algorithms are successfully employed in two applications related to human-computer interaction and pattern recognition: facial expression recognition and face detection.
International Joint Conference on Artificial IntelligenceSemi-supervised regression with co-training
244 Citations2005Zhi‐Hua Zhou, Ming Li
Experiments show that COREG can effectively exploit unlabeled data to improve regression estimates and is proposed as a co-training style semi-supervised regression algorithm.
Encyclopedia of Biometrics
243 Citations2009Stan Z. Li, Anil Jain +1 more
It is important to analyze the problem of anti-spoofing under the umbrella of biometric recognition systems to bring certain requirements in the database design, as well as adapted concepts for evaluation of biometrics recognition systems under spoofing attacks.
Large scale semi-supervised linear SVMs
233 Citations2006Vikas Sindhwani, S. Sathiya Keerthi
An implementation of Transductive SVM (TSVM) that is significantly more efficient and scalable than currently used dual techniques, for linear classification problems involving large, sparse datasets, and a variant of TSVM that involves multiple switching of labels.
Lecture notes in computer scienceAnalyzing Co-training Style Algorithms
219 Citations2007Wei Wang, Zhi‐Hua Zhou
It is shown that the co- training process can succeed even without two views, given that the two learners have large difference, which explains the success of some co-training style algorithms that do not require two views.
Democratic co-learning
201 Citations2005Yaojian Zhou, Shelley Goldman
This work presents democratic colearning in which multiple algorithms instead of multiple views enable learners to label data for each other, a new example selection method for active learning.
Co-EM support vector learning
200 Citations2004Ulf Brefeld, Tobias Scheffer
This work casts linear classifiers into a probabilistic framework and develops a co-EM version of the Support Vector Machine, which conducts experiments on text classification problems and compares the family of semi-supervised support vector algorithms under different conditions, including violations of the assumptions underlying multi-view learning.
IEEE Transactions on Knowledge and Data EngineeringSemisupervised Regression with Cotraining-Style Algorithms
200 Citations2007Zhi‐Hua Zhou, Ming Li
A cotraining-style semisupervised regression algorithm, that is, COREG, is proposed, which uses two regressors, each labels the unlabeled data for the other regressor, where the confidence in labeling an unlabeling example is estimated through the amount of reduction in mean squared error over the labeled neighborhood of that example.
A continuation method for semi-supervised SVMs
185 Citations2006Olivier Chapelle, Mingmin Chi +1 more
This paper proposes to use a global optimization technique known as continuation to alleviate the problem of non-convex optimization of S3VMs, which often results in suboptimal performances.
Lecture notes in computer scienceSETRED: Self-training with Editing
179 Citations2005Ming Li, Zhi‐Hua Zhou
Experiments show that the introduction of data editing is beneficial, and the learned hypotheses of Setred outperform those learned by the standard self-training algorithm.
Discriminant-EM algorithm with application to image retrieval
179 Citations2002Ying Wu, Qi Tian +1 more
The possibility of including an unlabeled data set to make up the insufficiency of labeled data is investigated and the proposed algorithm, Discriminant EM (D-EM) not only estimates the parameters of a generative model but also finds a linear transformation to relax the assumption of probabilistic structure of data distributions as well as select good features automatically.
Harmonic mixtures
169 Citations2005Xiaojin Zhu, John Lafferty
Experimental results show that this approach preserves the accuracy of purely graph-based transductive methods when the data has "manifold structure," and at the same time achieves inductive learning with significantly reduced computational cost.
Limitations of Co-Training for Natural Language Learning from Large Datasets
165 Citations2001David R. Pierce, Claire Cardie
This study examines the learning behavior of co-training on natural language processing tasks that typically require large numbers of training instances to achieve usable performance levels and proposes a moderately supervised variant of cotraining in which a human corrects the mistakes made during automatic labeling.
Journal of the American Statistical AssociationIterative Reclassification Procedure for Constructing an Asymptotically Optimal Rule of Allocation in Discriminant Analysis
163 Citations1975Geoffrey J. McLachlan
ACM Transactions on Information SystemsEnhancing relevance feedback in image retrieval using unlabeled data
154 Citations2006Zhi‐Hua Zhou, Kejia Chen +1 more
Experiments show that using semisupervised learning and active learning simultaneously in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.
Neural Information Processing SystemsSemi-supervised Learning via Gaussian Processes
149 Citations2004Neil D. Lawrence, Michael I. Jordan
A probabilistic approach to learning a Gaussian Process classifier in the presence of unlabeled data using a "null category noise model" (NCNM) inspired by ordered categorical noise models.
Bootstrapping
148 Citations2001Steven Abney
This chapter develops a new conception of the bootstrapping objection, according to which the problem with the idea that intentions provide reasons is that the problem with the idea that intentions provide reasons is that the authors find ourselves with a normative theory that issues incoherent advice.
The MIT Press eBooksManifold Denoising
148 Citations2007Matthias Hein, Markus Maier
The presented denoising algorithm is based on a graph-based diffusion process of the point sample and is analyzed using recent results about the convergence of graph Laplacians to improve the results of a semi-supervised learning algorithm.
Journal of the American Statistical AssociationNormal Discrimination with Unclassified Observations
144 Citations1978Terence J. O’Neill
Applying co-training methods to statistical parsing
144 Citations2001Anoop Sarkar
It is shown that training a statistical parser on the combined labeled and unlabeled data strongly out-performs training only on the labeled data.
Semi-supervised learning with very few labeled training examples
135 Citations2007Zhi‐Hua Zhou, De‐Chuan Zhan +1 more
By taking advantages of the correlations between the views using canonical component analysis, the proposed method can perform semi-supervised learning with only one labeled training example.
Unlabeled Data Can Degrade Classification Performance of Generative Classifiers
130 Citations2002Fábio Gagliardi Cozman, Ira L. Cohen
It is shown that unlabeled data can degrade the performance of a classifier when there are discrepancies between modeling assumptions used to build the classifier and the actual model that generates the data.
Deterministic annealing for semi-supervised kernel machines
129 Citations2006Vikas Sindhwani, S. Sathiya Keerthi +1 more
This paper presents a global optimization framework for semi-supervised Kernel machines where an easier problem is parametrically deformed to the original hard problem and minimizers are smoothly tracked.
Neural Information Processing SystemsProximity Graphs for Clustering and Manifold Learning
120 Citations2004Richard S. Zemel, Miguel Á. Carreira-Perpiñán
It is shown that a graph ensemble usually produces a better representation of the data manifold than standard methods; and that it provides robustness to a subsequent clustering or dimensionality reduction algorithm based on the graph.
A hybrid generative/discriminative approach to semi-supervised classifier design
106 Citations2005Akinori Fujino, Naonori Ueda +1 more
The proposed hybrid model is constructed by combining both generative and bias correction models based on the maximum entropy principle and significantly outperformed pureGenerative and discriminative methods when the classification performances of the both methods were comparable.
Lecture notes in computer scienceOnline Manifold Regularization: A New Learning Setting and Empirical Study
94 Citations2008Andrew B. Goldberg, Ming Li +1 more
An electro-optic passive display cell including two glass plates positioned spaced from each other with the edges thereof sealed and having an active constituent disposed between said plates.
Semi-supervised learning for structured output variables
80 Citations2006Ulf Brefeld, Tobias Scheffer
The co-training approach is based on the principle of maximizing the consensus among multiple independent hypotheses and developed into a semi-supervised support vector learning algorithm for joint input output spaces and arbitrary loss functions.
On multi-view active learning and the combination with semi-supervised learning
73 Citations2008Wei Wang, Zhi‐Hua Zhou
The empirical behavior of the two paradigms is studied, which verifies that the combination of multi-view active learning and semi-supervised learning is efficient.
Lecture notes in computer scienceExploiting Unlabeled Data in Content-Based Image Retrieval
64 Citations2004Zhi‐Hua Zhou, Kejia Chen +1 more
The SSAIR (Semi-Supervised Active Image Retrieval) approach, which attempts to exploit unlabeled data to improve the performance of content-based image retrieval (CBIR), is proposed and experiments show that semi-supervised learning and active learning mechanisms are both beneficial to CBIR.
The MIT Press eBooksHyperparameter Learning for Graph Based Semi-supervised Learning Algorithms
57 Citations2007Xinhua Zhang, Wee Sun Lee
This paper proposes a graph learning method for the harmonic energy minimization method by minimizing the leave-one-out prediction error on labeled data points and designed an efficient algorithm which significantly accelerates the calculation of the gradient by applying the matrix inversion lemma and using careful pre-computation.
Edinburgh Research Explorer (University of Edinburgh)Corrected Co-training for Statistical Parsers
55 Citations2003Rebecca Hwa, Miles Osborne +2 more
This work proposes a novel approach, which it is shown that this method requires only a third as many manual annotation decisions as corrected co-training/co-testing to achieve the same improvement in performance.
Lecture notes in computer scienceEstimation of Mixture Models Using Co-EM
55 Citations2005Steffen Bickel, Tobias Scheffer
This work studies estimation of mixture models for problems in which multiple views of the instances are available, and derives an algorithm that maximizes the likelihood and the consensus among models in the individual views.
Communications in Statistics - Simulation and ComputationUpdating a discriminant function on the basis of unclassified data
52 Citations1982Geoffrey J. McLachlan, S. Gànesalingam
A new semi-supervised EM algorithm for image retrieval
50 Citations2003Anlei Dong, Bir Bhanu
This paper presents a new semi-supervised EM algorithm (NSSEM), where the image distribution in feature space is modeled as a mixture of Gaussian densities, which helps to improve retrieval performance.
Information Processing & ManagementSemi-supervised document retrieval
46 Citations2008Ming Li, Hang Li +1 more
Experimental results indicate that SSRank consistently and almost always significantly outperforms the baseline methods, given the same amount of labeled data, because SSRank can effectively leverage the use of unlabeled data in learning.
Lecture notes in computer scienceLearning with Unlabeled Data and Its Application to Image Retrieval
38 Citations2006Zhi‐Hua Zhou
Knowledge and Information SystemsSemisupervised learning from different information sources
35 Citations2004Tao Li, Mitsunori Ogihara
A semisupervised learning approach is proposed that attempts to minimise this disagreement by employing a co-updating method and making use of both labeled and unlabeled data.
Semi-supervised learning with sparse grids
35 Citations2005Jochen Garcke, Michael Griebel
This article formulate the semi-supervised learning problem by a regularization approach and discretize the resulting problem in function space by the sparse grid method and solve the arising equations using the so-called combination technique.
Knowledge and Information SystemsSemi-supervised learning with an imperfect supervisor
34 Citations2005Massih R. Amini, Patrick Gallinari
Semi-supervised learning by disagreement
31 Citations2008Zhi‐Hua Zhou
Some of the research advances in disagreement-based semi-supervised learning are introduced, a paradigm that covers a broad range of algorithms and has been successfully applied to many real tasks such as statistical parsing, noun phrase identification, image retrieval, etc.
Lecture notes in computer scienceUsing Transduction and Multi-view Learning to Answer Emails
25 Citations2003Michael Kockelkorn, Andreas Lüneburg +1 more
This work addresses the problem of predicting which of several frequently used answers a user will choose to respond to an email, and effectively utilizes the data that is typically available in this setting: inbound and outbound emails stored on a server.
Multi-View Hidden Markov Perceptrons.
15 Citations2005Ulf Brefeld, Christoph Büscher +1 more
The multi-view approach is based on the principle of maximizing the consensus among multiple independent hypotheses and developed into a semisupervised hidden Markov perceptron algorithm, which utilizes unlabeled data effectively and discriminates more accurately than its purely supervised counterparts.
Lecture notes in computer scienceBudget Semi-supervised Learning
10 Citations2009Zhi‐Hua Zhou, Michael K. Ng +2 more
Budget semi-supervised learning with a resource budget, such as a limited memory insufficient to accommodate and/or process all available unlabeled data is proposed, and it is shown that this is achievable by a simple yet effective method.
…