intTypePromotion=4
ADSENSE

Semi-supervised learning methods

Xem 1-8 trên 8 kết quả Semi-supervised learning methods
  • The amount of unlabeled linguistic data available to us is much larger and growing much faster than the amount of labeled data. Semi-supervised learning algorithms combine unlabeled data with a small labeled training set to train better models. This tutorial emphasizes practical applications of semisupervised learning; we treat semi-supervised learning methods as tools for building effective models from limited training data. An attendee will leave our tutorial with 1.

    pdf1p hongphan_1 15-04-2013 31 1   Download

  • Shortage of manually sense-tagged data is an obstacle to supervised word sense disambiguation methods. In this paper we investigate a label propagation based semisupervised learning algorithm for WSD, which combines labeled and unlabeled data in learning process to fully realize a global consistency assumption: similar examples should have similar labels.

    pdf8p bunbo_1 17-04-2013 26 1   Download

  • The natural way overcoming the information loss of the above assumption is to represent the gene expression data as the hypergraph. Thus, in this paper, the three un-normalized, random walk, and symmetric normalized hypergraph Laplacian based semisupervised learning methods applied to hypergraph constructed from the gene expression data in order to predict the functions of yeast proteins are introduced.

    pdf7p praishy2 27-02-2019 6 0   Download

  • We initiate a study comparing effectiveness of the transformed spaces learned by recently proposed supervised, and semisupervised metric learning algorithms to those generated by previously proposed unsupervised dimensionality reduction methods (e.g., PCA). Through a variety of experiments on different realworld datasets, we find IDML-IT, a semisupervised metric learning algorithm to be the most effective.

    pdf5p hongdo_1 12-04-2013 48 3   Download

  • Partial cognates are pairs of words in two languages that have the same meaning in some, but not all contexts. Detecting the actual meaning of a partial cognate in context can be useful for Machine Translation tools and for Computer-Assisted Language Learning tools. In this paper we propose a supervised and a semisupervised method to disambiguate partial cognates between two languages: French and English. The methods use only automatically-labeled data; therefore they can be applied for other pairs of languages as well.

    pdf8p hongvang_1 16-04-2013 39 1   Download

  • In this paper, we present a method for guessing POS tags of unknown words using local and global information. Although many existing methods use only local information (i.e. limited window size or intra-sentential features), global information (extra-sentential features) provides valuable clues for predicting POS tags of unknown words. We propose a probabilistic model for POS guessing of unknown words using global information as well as local information, and estimate its parameters using Gibbs sampling.

    pdf8p hongvang_1 16-04-2013 31 2   Download

  • This paper proposes a semi-supervised boosting approach to improve statistical word alignment with limited labeled data and large amounts of unlabeled data. The proposed approach modifies the supervised boosting algorithm to a semisupervised learning algorithm by incorporating the unlabeled data. In this algorithm, we build a word aligner by using both the labeled data and the unlabeled data. Then we build a pseudo reference set for the unlabeled data, and calculate the error rate of each word aligner using only the labeled data.

    pdf8p hongvang_1 16-04-2013 28 1   Download

  • Most attempts to train part-of-speech taggers on a mixture of labeled and unlabeled data have failed. In this work stacked learning is used to reduce tagging to a classification task. This simplifies semisupervised training considerably. Our prefered semi-supervised method combines tri-training (Li and Zhou, 2005) and disagreement-based co-training. On the Wall Street Journal, we obtain an error reduction of 4.2% with SVMTool (Gimenez and Marquez, 2004).

    pdf4p hongdo_1 12-04-2013 39 2   Download

CHỦ ĐỀ BẠN MUỐN TÌM

TOP DOWNLOAD
368 tài liệu
1140 lượt tải
ADSENSE

p_strKeyword=Semi-supervised learning methods
p_strCode=semisupervisedlearningmethods

nocache searchPhinxDoc

 

Đồng bộ tài khoản
2=>2