9,640 research outputs found
SEVEN: Deep Semi-supervised Verification Networks
Verification determines whether two samples belong to the same class or not,
and has important applications such as face and fingerprint verification, where
thousands or millions of categories are present but each category has scarce
labeled examples, presenting two major challenges for existing deep learning
models. We propose a deep semi-supervised model named SEmi-supervised
VErification Network (SEVEN) to address these challenges. The model consists of
two complementary components. The generative component addresses the lack of
supervision within each category by learning general salient structures from a
large amount of data across categories. The discriminative component exploits
the learned general features to mitigate the lack of supervision within
categories, and also directs the generative component to find more informative
structures of the whole data manifold. The two components are tied together in
SEVEN to allow an end-to-end training of the two components. Extensive
experiments on four verification tasks demonstrate that SEVEN significantly
outperforms other state-of-the-art deep semi-supervised techniques when labeled
data are in short supply. Furthermore, SEVEN is competitive with fully
supervised baselines trained with a larger amount of labeled data. It indicates
the importance of the generative component in SEVEN.Comment: 7 pages, 2 figures, accepted to the 2017 International Joint
Conference on Artificial Intelligence (IJCAI-17
Toward Optimal Feature Selection in Naive Bayes for Text Categorization
Automated feature selection is important for text categorization to reduce
the feature size and to speed up the learning process of classifiers. In this
paper, we present a novel and efficient feature selection framework based on
the Information Theory, which aims to rank the features with their
discriminative capacity for classification. We first revisit two information
measures: Kullback-Leibler divergence and Jeffreys divergence for binary
hypothesis testing, and analyze their asymptotic properties relating to type I
and type II errors of a Bayesian classifier. We then introduce a new divergence
measure, called Jeffreys-Multi-Hypothesis (JMH) divergence, to measure
multi-distribution divergence for multi-class classification. Based on the
JMH-divergence, we develop two efficient feature selection methods, termed
maximum discrimination () and methods, for text categorization.
The promising results of extensive experiments demonstrate the effectiveness of
the proposed approaches.Comment: This paper has been submitted to the IEEE Trans. Knowledge and Data
Engineering. 14 pages, 5 figure
Multi-Document Summarization via Discriminative Summary Reranking
Existing multi-document summarization systems usually rely on a specific
summarization model (i.e., a summarization method with a specific parameter
setting) to extract summaries for different document sets with different
topics. However, according to our quantitative analysis, none of the existing
summarization models can always produce high-quality summaries for different
document sets, and even a summarization model with good overall performance may
produce low-quality summaries for some document sets. On the contrary, a
baseline summarization model may produce high-quality summaries for some
document sets. Based on the above observations, we treat the summaries produced
by different summarization models as candidate summaries, and then explore
discriminative reranking techniques to identify high-quality summaries from the
candidates for difference document sets. We propose to extract a set of
candidate summaries for each document set based on an ILP framework, and then
leverage Ranking SVM for summary reranking. Various useful features have been
developed for the reranking process, including word-level features,
sentence-level features and summary-level features. Evaluation results on the
benchmark DUC datasets validate the efficacy and robustness of our proposed
approach
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