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NIPS
2008
9 years 8 months ago
Unlabeled data: Now it helps, now it doesn't
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
NIPS
2008
9 years 8 months ago
Semi-supervised Learning with Weakly-Related Unlabeled Data: Towards Better Text Categorization
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
IJCAI
2007
9 years 8 months ago
Detecting Changes in Unlabeled Data Streams Using Martingale
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
IJCAI
2007
9 years 8 months ago
Optimistic Active-Learning Using Mutual Information
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
ICMLA
2007
9 years 8 months ago
Semi-Supervised Active Learning for Modeling Medical Concepts from Free Text
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...
SDM
2010
SIAM
226views Data Mining» more  SDM 2010»
9 years 8 months ago
Two-View Transductive Support Vector Machines
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
EMNLP
2007
9 years 8 months ago
Semi-Supervised Structured Output Learning Based on a Hybrid Generative and Discriminative Approach
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
COLING
2008
9 years 8 months ago
Homotopy-Based Semi-Supervised Hidden Markov Models for Sequence Labeling
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
COLING
2008
9 years 8 months ago
Re-estimation of Lexical Parameters for Treebank PCFGs
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
COLING
2008
9 years 8 months ago
Learning Reliable Information for Dependency Parsing Adaptation
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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