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253
Semi-Supervised Learning Literature Survey
, 2006
"... We review the literature on semi-supervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole
spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semi-supervised learning. This document is a chapter ..."
Abstract
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Cited by 757 (8 self)
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We review the literature on semi-supervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole
spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semi-supervised learning. This document is a chapter excerpt from the author’s
doctoral thesis (Zhu, 2005). However the author plans to update the online version frequently to incorporate the latest development in the field. Please obtain the latest
version at http://www.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf
Creating Subjective and Objective Sentence Classifiers from Unannotated Texts
- INTELLIGENT TEXT PROCESSING (CICLING-05)
, 2005
"... This paper presents the results of developing subjectivity classifiers using only unannotated texts for training. The performance rivals that of previous supervised learning approaches. In addition, we advance the state of the art in objective sentence classification by learning extraction patterns ..."
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Cited by 162 (10 self)
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This paper presents the results of developing subjectivity classifiers using only unannotated texts for training. The performance rivals that of previous supervised learning approaches. In addition, we advance the state of the art in objective sentence classification by learning extraction patterns associated with objectivity and creating objective classifiers that achieve substantially higher recall than previous work with comparable precision.
Active + Semi-Supervised Learning = Robust Multi-View Learning
- Proceedings of ICML-02, 19th International Conference on Machine Learning
, 2002
"... In a multi-view problem, the features of the domain can be partitioned into disjoint subsets (views) that are sufficient to learn the target concept. ..."
Abstract
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Cited by 108 (7 self)
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In a multi-view problem, the features of the domain can be partitioned into disjoint subsets (views) that are sufficient to learn the target concept.
Semi-supervised Learning by Entropy Minimization
"... We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regularization, which enables to incorporate unlabeled data in the standard supervised learning. This regularizer can be applied to ..."
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Cited by 101 (2 self)
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We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regularization, which enables to incorporate unlabeled data in the standard supervised learning. This regularizer can be applied to any model of posterior probabilities. Our approach provides a new motivation for some existing semi-supervised learning algorithms which are particular or limiting instances of minimum entropy regularization. A series of experiments illustrates that the proposed solution benefits from unlabeled data. The method challenges mixture models when the data are sampled from the distribution class spanned by the generative model. The performances are definitely in favor of minimum entropy regularization when generative models are misspecified, and the weighting of unlabeled data provides robustness to the violation of the “cluster assumption”. Finally, we also illustrate that the method can be far superior to manifold learning in high dimension spaces, and also when the manifolds are generated by moving examples along the discriminating directions.
GraphLab: A New Framework For Parallel Machine Learning
"... Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design chal ..."
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Cited by 85 (1 self)
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Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. By targeting common patterns in ML, we developed GraphLab, which improves upon abstractions like MapReduce by compactly expressing asynchronous iterative algorithms with sparse computational dependencies while ensuring data consistency and achieving a high degree of parallel performance. We demonstrate the expressiveness of the GraphLab framework by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing. We show that using GraphLab we can achieve excellent parallel performance on large scale real-world problems. 1
Co-Training and Expansion: Towards Bridging Theory and Practice
, 2004
"... Co-training is a method for combining labeled and unlabeled data when examples can be thought of as containing two distinct sets of features. It has had a number of practical successes, yet previous theoretical analyses have needed very strong assumptions on the data that are unlikely to be sati ..."
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Cited by 76 (5 self)
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Co-training is a method for combining labeled and unlabeled data when examples can be thought of as containing two distinct sets of features. It has had a number of practical successes, yet previous theoretical analyses have needed very strong assumptions on the data that are unlikely to be satisfied in practice.
Using Unlabeled Data to Improve Text Classification
, 2001
"... One key difficulty with text classification learning algorithms is that they require many hand-labeled examples to learn accurately. This dissertation demonstrates that supervised learning algorithms that use a small number of labeled examples and many inexpensive unlabeled examples can create high- ..."
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Cited by 70 (0 self)
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One key difficulty with text classification learning algorithms is that they require many hand-labeled examples to learn accurately. This dissertation demonstrates that supervised learning algorithms that use a small number of labeled examples and many inexpensive unlabeled examples can create high-accuracy text classifiers. By assuming that documents are created by a parametric generative model, Expectation-Maximization (EM) finds local maximum a posteriori models and classifiers from all the data -- labeled and unlabeled. These generative models do not capture all the intricacies of text; however on some domains this technique substantially improves classification accuracy, especially when labeled data are sparse. Two problems arise from this basic approach. First, unlabeled data can hurt performance in domains where the generative modeling assumptions are too strongly violated. In this case the assumptions can be made more representative in two ways: by modeling sub-topic class structure, and by modeling super-topic hierarchical class relationships. By doing so, model probability and classification accuracy come into correspondence, allowing unlabeled data to improve classification performance. The second problem is that even with a representative model, the improvements given by unlabeled data do not sufficiently compensate for a paucity of labeled data. Here, limited labeled data provide EM initializations that lead to low-probability models. Performance can be significantly improved by using active learning to select high-quality initializations, and by using alternatives to EM that avoid low-probability local maxima.
Translated learning: Transfer learning across different feature spaces
- in Proc. NIPS
"... This paper investigates a new machine learning strategy called translated learn-ing. Unlike many previous learning tasks, we focus on how to use labeled data from one feature space to enhance the classification of other entirely different learning spaces. For example, we might wish to use labeled te ..."
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Cited by 60 (3 self)
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This paper investigates a new machine learning strategy called translated learn-ing. Unlike many previous learning tasks, we focus on how to use labeled data from one feature space to enhance the classification of other entirely different learning spaces. For example, we might wish to use labeled text data to help learn a model for classifying image data, when the labeled images are difficult to ob-tain. An important aspect of translated learning is to build a “bridge ” to link one feature space (known as the “source space”) to another space (known as the “tar-get space”) through a translator in order to migrate the knowledge from source to target. The translated learning solution uses a language model to link the class labels to the features in the source spaces, which in turn is translated to the fea-tures in the target spaces. Finally, this chain of linkages is completed by tracing back to the instances in the target spaces. We show that this path of linkage can be modeled using a Markov chain and risk minimization. Through experiments on the text-aided image classification and cross-language classification tasks, we demonstrate that our translated learning framework can greatly outperform many state-of-the-art baseline methods. 1
Email Classification with Co-Training
, 2002
"... The main problems in text classification are lack of labeled data, as well as the cost of labeling the unlabeled data. We address these problems by exploring co-training - an algorithm that uses unlabeled data along with a few labeled examples to boost the performance of a classifier. We experiment ..."
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Cited by 57 (0 self)
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The main problems in text classification are lack of labeled data, as well as the cost of labeling the unlabeled data. We address these problems by exploring co-training - an algorithm that uses unlabeled data along with a few labeled examples to boost the performance of a classifier. We experiment with co-training on the email domain. Our results show that the performance of co-training depends on the learning algorithm it uses. In particular, Support Vector Machines significantly outperforms Naive Bayes on email classification.
Using LSI for Text Classification in the Presence of Background Text
- PROCEEDINGS OF CIKM-01, 10TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT
, 2001
"... This paper presents work that uses Latent Semantic Indexing (LSI) for text classification. However, in addition to relying on labeled training data, we improve classification accuracy by also using unlabeled data and other forms of available "background" text in the classification proces ..."
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Cited by 54 (4 self)
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This paper presents work that uses Latent Semantic Indexing (LSI) for text classification. However, in addition to relying on labeled training data, we improve classification accuracy by also using unlabeled data and other forms of available "background" text in the classification process. Rather than performing LSI's singular value decomposition (SVD) process solely on the training data, we instead use an expandedterm-by-document matrix that includes both the labeled data as well as any available and relevant background text. We report the performance of this approach on data sets both with and without the inclusion of the background text, and compare our work to other efforts that can incorporate unlabeled data and other background text in the classification process.
