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    NIPS
    2008
    215views Information Technology» more  NIPS 2008»
    Unlabeled data: Now it helps, now it doesn't
    13 years 6 months ago
    Unlabeled data: Now it helps, now it doesn't
    Download  www.cs.cmu.edu
    Empirical evidence shows that in favorable situations semi-supervised learning (SSL) algorithms can capitalize on the abundance of unlabeled training data to improve the performan...
    Aarti Singh, Robert D. Nowak, Xiaojin Zhu
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    NIPS
    2008
    143views Information Technology» more  NIPS 2008»
    Semi-supervised Learning with Weakly-Related Unlabeled Data: Towards Better Text Categorization
    13 years 6 months ago
    Semi-supervised Learning with Weakly-Related Unlabeled Data: Towards Better Text Categorization
    Download  www.cs.cmu.edu
    The cluster assumption is exploited by most semi-supervised learning (SSL) methods. However, if the unlabeled data is merely weakly related to the target classes, it becomes quest...
    Liu Yang, Rong Jin, Rahul Sukthankar
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    IJCAI
    2007
    215views Artificial Intelligence» more  IJCAI 2007»
    Detecting Changes in Unlabeled Data Streams Using Martingale
    13 years 6 months ago
    Detecting Changes in Unlabeled Data Streams Using Martingale
    Download  www.cs.gmu.edu
    The martingale framework for detecting changes in data stream, currently only applicable to labeled data, is extended here to unlabeled data using clustering concept. The one-pass...
    Shen-Shyang Ho, Harry Wechsler
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    IJCAI
    2007
    172views Artificial Intelligence» more  IJCAI 2007»
    Optimistic Active-Learning Using Mutual Information
    13 years 6 months ago
    Optimistic Active-Learning Using Mutual Information
    Download  www.ijcai.org
    An “active learning system” will sequentially decide which unlabeled instance to label, with the goal of efficiently gathering the information necessary to produce a good cla...
    Yuhong Guo, Russell Greiner
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    ICMLA
    2007
    192views Machine Learning» more  ICMLA 2007»
    Semi-Supervised Active Learning for Modeling Medical Concepts from Free Text
    13 years 6 months ago
    Semi-Supervised Active Learning for Modeling Medical Concepts from Free Text
    Download  people.csail.mit.edu
    We apply a new active learning formulation to the problem of learning medical concepts from unstructured text. The new formulation is based on maximizing the mutual information th...
    Rómer Rosales, Praveen Krishnamurthy, R. Bh...
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    SDM
    2010
    SIAM
    226views Data Mining» more  SDM 2010»
    Two-View Transductive Support Vector Machines
    13 years 6 months ago
    Two-View Transductive Support Vector Machines
    Download  www.cais.ntu.edu.sg
    Obtaining high-quality and up-to-date labeled data can be difficult in many real-world machine learning applications, especially for Internet classification tasks like review spam...
    Guangxia Li, Steven C. H. Hoi, Kuiyu Chang
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    EMNLP
    2007
    250views Natural Language Processing» more  EMNLP 2007»
    Semi-Supervised Structured Output Learning Based on a Hybrid Generative and Discriminative Approach
    13 years 6 months ago
    Semi-Supervised Structured Output Learning Based on a Hybrid Generative and Discriminative Approach
    Download  acl.ldc.upenn.edu
    This paper proposes a framework for semi-supervised structured output learning (SOL), specifically for sequence labeling, based on a hybrid generative and discriminative approach...
    Jun Suzuki, Akinori Fujino, Hideki Isozaki
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    COLING
    2008
    159views Computational Linguistics» more  COLING 2008»
    Homotopy-Based Semi-Supervised Hidden Markov Models for Sequence Labeling
    13 years 6 months ago
    Homotopy-Based Semi-Supervised Hidden Markov Models for Sequence Labeling
    Download  www.cs.sfu.ca
    This paper explores the use of the homotopy method for training a semi-supervised Hidden Markov Model (HMM) used for sequence labeling. We provide a novel polynomial-time algorith...
    Gholamreza Haffari, Anoop Sarkar
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    COLING
    2008
    130views Computational Linguistics» more  COLING 2008»
    Re-estimation of Lexical Parameters for Treebank PCFGs
    13 years 6 months ago
    Re-estimation of Lexical Parameters for Treebank PCFGs
    Download  aclweb.org
    We present procedures which pool lexical information estimated from unlabeled data via the Inside-Outside algorithm, with lexical information from a treebank PCFG. The procedures ...
    Tejaswini Deoskar
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    COLING
    2008
    130views Computational Linguistics» more  COLING 2008»
    Learning Reliable Information for Dependency Parsing Adaptation
    13 years 6 months ago
    Learning Reliable Information for Dependency Parsing Adaptation
    Download  www.aclweb.org
    In this paper, we focus on the adaptation problem that has a large labeled data in the source domain and a large but unlabeled data in the target domain. Our aim is to learn relia...
    Wenliang Chen, Youzheng Wu, Hitoshi Isahara
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