• Title, Summary, Keyword: 준지도 학습

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Utilizing Unlabeled Documents in Automatic Classification with Inter-document Similarities (문헌간 유사도를 이용한 자동분류에서 미분류 문헌의 활용에 관한 연구)

  • Kim, Pan-Jun;Lee, Jae-Yun
    • Journal of the Korean Society for information Management
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    • v.24 no.1
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    • pp.251-271
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    • 2007
  • This paper studies the problem of classifying documents with labeled and unlabeled learning data, especially with regards to using document similarity features. The problem of using unlabeled data is practically important because in many information systems obtaining training labels is expensive, while large quantities of unlabeled documents are readily available. There are two steps In general semi-supervised learning algorithm. First, it trains a classifier using the available labeled documents, and classifies the unlabeled documents. Then, it trains a new classifier using all the training documents which were labeled either manually or automatically. We suggested two types of semi-supervised learning algorithm with regards to using document similarity features. The one is one step semi-supervised learning which is using unlabeled documents only to generate document similarity features. And the other is two step semi-supervised learning which is using unlabeled documents as learning examples as well as similarity features. Experimental results, obtained using support vector machines and naive Bayes classifier, show that we can get improved performance with small labeled and large unlabeled documents then the performance of supervised learning which uses labeled-only data. When considering the efficiency of a classifier system, the one step semi-supervised learning algorithm which is suggested in this study could be a good solution for improving classification performance with unlabeled documents.

Sentiment Classification for Korean Tweets via Semi-Supervised Learning (준지도 학습을 이용한 트윗 감정 분류)

  • Seo, Hyeong-Won;Noh, Kyung-Mok;Cheon, Min-A;Kim, Jae-Hoon
    • Annual Conference on Human and Language Technology
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    • pp.123-125
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    • 2012
  • 본 논문은 기계 학습을 이용한 감정 분류에 필요한 학습 말뭉치를 효율적으로 확장하는 방법에 대하여 기술한다. 학습 말뭉치는 일반적으로 그에 알맞은 레이블을 정해야 하는데, 그 양이 어마어마하기 때문에 이 과정을 일일이 사람이 할 수는 없다. 그에 대한 해결책으로써 이미 많은 준지도학습 방법이 연구되었고, 그것을 트윗이라는 짧은 문서를 감정 분류하는 것에 적용해도 감정 문서 분류기의 성능이 좋다는 결과를 확인하였다.

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Smoothing parameter selection in semi-supervised learning (준지도 학습의 모수 선택에 관한 연구)

  • Seok, Kyungha
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.4
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    • pp.993-1000
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    • 2016
  • Semi-supervised learning makes it easy to use an unlabeled data in the supervised learning such as classification. Applying the semi-supervised learning on the regression analysis, we propose two methods for a better regression function estimation. The proposed methods have been assumed different marginal densities of independent variables and different smoothing parameters in unlabeled and labeled data. We shows that the overfitted pilot estimator should be used to achieve the fastest convergence rate and unlabeled data may help to improve the convergence rate with well estimated smoothing parameters. We also find the conditions of smoothing parameters to achieve optimal convergence rate.

Semi-Supervised Learning by Gaussian Mixtures (정규 혼합분포를 이용한 준지도 학습)

  • Choi, Byoung-Jeong;Chae, Youn-Seok;Choi, Woo-Young;Park, Chang-Yi;Koo, Ja-Yong
    • The Korean Journal of Applied Statistics
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    • v.21 no.5
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    • pp.825-833
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    • 2008
  • Discriminant analysis based on Gaussian mixture models, an useful tool for multi-class classifications, can be extended to semi-supervised learning. We consider a model selection problem for a Gaussian mixture model in semi-supervised learning. More specifically, we adopt Bayesian information criterion to determine the number of subclasses in the mixture model. Through simulations, we illustrate the usefulness of the criterion.

Response Modeling with Semi-Supervised Support Vector Regression (준지도 지지 벡터 회귀 모델을 이용한 반응 모델링)

  • Kim, Dong-Il
    • Journal of the Korea Society of Computer and Information
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    • v.19 no.9
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    • pp.125-139
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    • 2014
  • In this paper, I propose a response modeling with a Semi-Supervised Support Vector Regression (SS-SVR) algorithm. In order to increase the accuracy and profit of response modeling, unlabeled data in the customer dataset are used with the labeled data during training. The proposed SS-SVR algorithm is designed to be a batch learning to reduce the training complexity. The label distributions of unlabeled data are estimated in order to consider the uncertainty of labeling. Then, multiple training data are generated from the unlabeled data and their estimated label distributions with oversampling to construct the training dataset with the labeled data. Finally, a data selection algorithm, Expected Margin based Pattern Selection (EMPS), is employed to reduce the training complexity. The experimental results conducted on a real-world marketing dataset showed that the proposed response modeling method trained efficiently, and improved the accuracy and the expected profit.

Automatic Text Categorization based on Semi-Supervised Learning (준지도 학습 기반의 자동 문서 범주화)

  • Ko, Young-Joong;Seo, Jung-Yun
    • Journal of KIISE:Software and Applications
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    • v.35 no.5
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    • pp.325-334
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    • 2008
  • The goal of text categorization is to classify documents into a certain number of pre-defined categories. The previous studies in this area have used a large number of labeled training documents for supervised learning. One problem is that it is difficult to create the labeled training documents. While it is easy to collect the unlabeled documents, it is not so easy to manually categorize them for creating training documents. In this paper, we propose a new text categorization method based on semi-supervised learning. The proposed method uses only unlabeled documents and keywords of each category, and it automatically constructs training data from them. Then a text classifier learns with them and classifies text documents. The proposed method shows a similar degree of performance, compared with the traditional supervised teaming methods. Therefore, this method can be used in the areas where low-cost text categorization is needed. It can also be used for creating labeled training documents.

Graph Construction Based on Fast Low-Rank Representation in Graph-Based Semi-Supervised Learning (그래프 기반 준지도 학습에서 빠른 낮은 계수 표현 기반 그래프 구축)

  • Oh, Byonghwa;Yang, Jihoon
    • Journal of KIISE
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    • v.45 no.1
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    • pp.15-21
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    • 2018
  • Low-Rank Representation (LRR) based methods are widely used in many practical applications, such as face clustering and object detection, because they can guarantee high prediction accuracy when used to constructing graphs in graph - based semi-supervised learning. However, in order to solve the LRR problem, it is necessary to perform singular value decomposition on the square matrix of the number of data points for each iteration of the algorithm; hence the calculation is inefficient. To solve this problem, we propose an improved and faster LRR method based on the recently published Fast LRR (FaLRR) and suggests ways to introduce and optimize additional constraints on the underlying optimization goals in order to address the fact that the FaLRR is fast but actually poor in classification problems. Our experiments confirm that the proposed method finds a better solution than LRR does. We also propose Fast MLRR (FaMLRR), which shows better results when the goal of minimizing is added.

Semi-supervised classification with LS-SVM formulation (최소제곱 서포터벡터기계 형태의 준지도분류)

  • Seok, Kyung-Ha
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.3
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    • pp.461-470
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    • 2010
  • Semi supervised classification which is a method using labeled and unlabeled data has considerable attention in recent years. Among various methods the graph based manifold regularization is proved to be an attractive method. Least squares support vector machine is gaining a lot of popularities in analyzing nonlinear data. We propose a semi supervised classification algorithm using the least squares support vector machines. The proposed algorithm is based on the manifold regularization. In this paper we show that the proposed method can use unlabeled data efficiently.

A study on semi-supervised kernel ridge regression estimation (준지도 커널능형회귀모형에 관한 연구)

  • Seok, Kyungha
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.2
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    • pp.341-353
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    • 2013
  • In many practical machine learning and data mining applications, unlabeled data are inexpensive and easy to obtain. Semi-supervised learning try to use such data to improve prediction performance. In this paper, a semi-supervised regression method, semi-supervised kernel ridge regression estimation, is proposed on the basis of kernel ridge regression model. The proposed method does not require a pilot estimation of the label of the unlabeled data. This means that the proposed method has good advantages including less number of parameters, easy computing and good generalization ability. Experiments show that the proposed method can effectively utilize unlabeled data to improve regression estimation.

The Construction of a Domain-Specific Sentiment Dictionary Using Graph-based Semi-supervised Learning Method (그래프 기반 준지도 학습 방법을 이용한 특정분야 감성사전 구축)

  • Kim, Jung-Ho;Oh, Yean-Ju;Chae, Soo-Hoan
    • Science of Emotion and Sensibility
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    • v.18 no.1
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    • pp.103-110
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    • 2015
  • Sentiment lexicon is an essential element for expressing sentiment on a text or recognizing sentiment from a text. We propose a graph-based semi-supervised learning method to construct a sentiment dictionary as sentiment lexicon set. In particular, we focus on the construction of domain-specific sentiment dictionary. The proposed method makes up a graph according to lexicons and proximity among lexicons, and sentiments of some lexicons which already know their sentiment values are propagated throughout all of the lexicons on the graph. There are two typical types of the sentiment lexicon, sentiment words and sentiment phrase, and we construct a sentiment dictionary by creating each graph of them and infer sentiment of all sentiment lexicons. In order to verify our proposed method, we constructed a sentiment dictionary specific to the movie domain, and conducted sentiment classification experiments with it. As a result, it have been shown that the classification performance using the sentiment dictionary is better than the other using typical general-purpose sentiment dictionary.