Y. Yang and C. G. Chute. An application of Least Squares Fit mapping to text information retrieval. In R. Korfhage, E. Rasmussen, and P. Willett, editors, Development in Information Retrieval, pages 281--290, Pittsburgh, US, 1993. ACM Press, New York, US. An extended version appears as [139].  Home/Search   Document Not in Database   Summary   Related Articles   Check

....method is the use of the Genetic Algorithm (GA) 68] a general optimization method based on genetic adaptation, to optimize how well the retrieval system is performing. Examples include Gordon s [56] application to adaptively determining the best document representations, and Yang at al. s [166] query optimization. The Genetic Algorithm modifies parameters of the documents or queries in order to optimize a measure of how well the retrieval system is performing. In both cases, the authors use variations of standard IR measures to measure system performance in the GA. For example, Yang et ....

....each represent documents in a vector space such that two document vectors may be similar even if they share no terms. These methods include Latent Semantic Indexing [32] Gallant s context vector method [51] Brauen s adaptive document vectors [15] and Yang Chute s canonical concept mapping [166] [165] most of which were reviewed in Chapter 2. 3.1.2 Metric Similarity Modeling The approach we propose, Metric Similarity Modeling (MSM) also uses a multidimensional semantic space in which to represent documents. Semantically related documents are represented by similar vectors, and ....

[Article contains additional citation context not shown here]

Yiming Yang and Christopher G. Chute. An application of least squares fit mapping to text information retrieval. In Proceedings of the ACM SIGIR, pages 281--290, Pittsburgh, PA, June 1993. 163

....A number of other researchers are using related linear algebra methods for information retrieval and classification work. Schutze [27] and Gallant [14] have used SVD and related dimension reduction ideas for word sense disambiguation and information retrieval work. Hull [17] and Yang and Chute [29] have used LSI SVD as the first step in conjunction with statistical classification (e.g. discriminant analysis) Using the LSI derived dimensions effectively reduces the number of predictor variables for classification. Wu et al. in [28] also used LSI SVD to reduce the training set dimension for ....

Y. YANG AND C. G. CHUTE, An application of least squares fit mapping to text information retrieval, in Proceedings of the Sixteenth Annual International ACM-SIGIR Conference, 1993, pp. 281--290.

.... Ringuette 1994) and the Expectation Maximization (EM) algorithm (Dempster, Laird, Rubin 1977) The naive Bayes algorithm is a one of a class of statistical text classifiers that use word frequencies as features. Other examples include Rocchio (Salton 1991; Rocchio 1971) regression models (Yang Chute 1993), k nearest neighbor (Yang Pederson 1997) and Support Vector Machines (Joachims 1997b) EM is a class of iterative algorithms for maximum likelihood estimation in problems with incomplete data. The result is an algorithm that extends conventional text learning algorithms by using EM to ....

Yang, Y., and Chute, C. G. 1993. An application of least squares fit mapping to text information retrieval.

....algorithms are not scaleable with the size of feature set, which is expressed in the order of tens of thousands. This requires reduction of feature set or training set in such a way that the accuracy would not degrade. On the other hand, algorithms like k NN [8] and Linear Least Squares Fit (LLSF) [9] mapping method can be used with large set of features compared to the other existing methods. This paper examines the performance of a new version of the nearest neighbor algorithm, which is called k NNFP [3] when applied to text categorization. The k NNFP classifier is a variant of k NN. The ....

Y. Yang and CG. Chute. An Application of Least Squares Fit Mapping to Text Information Retrieval. Proceedings of the sixteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval , 281-290, 1993.

....1994 ] and the Expectation Maximization (EM) algorithm [ Dempster et al. 1977 ] The naive Bayes algorithm is one of a class of statistical text classifiers that uses word frequencies as features. Other examples include TFIDF Rocchio [ Salton, 1991; Rocchio, 1971 ] regression models [ Yang and Chute, 1993 ] k nearest neighbor [ Yang and Pederson, 1997 ] and Support Vector Machines [ Joachims, 1997b ] EM is a class of iterative algorithms for maximum likelihood estimation in problems with incomplete data. The result of combining these two is an algorithm that extends conventional text learning ....

Yiming Yang and Christopher G. Chute. An application of least squares fit mapping to text information retrieval. In Proceedings of the Sixteenth Annual International ACM SIGIR Conference, 1993.

....some consequences for subsequent processing which are not discussed in detail here. However, the essential consequence is that the resulting decision vector can not be normalized to length 1. The linear classifier is identical to the LLSF (linear least square fit) classifier described by Yang (see [7] and [8] However, the mathematical principle is different in general if higher order polynomials are used. In this case, a non linear function (e.g. quadratic polynomial) maps the feature space to the decision space yielding better separation of categories in the decision space. 4 Experiments ....

Y. Y. Yang and C. G. Chute, An Application of Least Squares Fit Mapping To Text Information Retrieval, Proceedings, 16th Int. ACMSIGIR Conf. on Research and Development in Information Retrieval, Pittsburgh, PA, 1993.

....wellknown learning algorithms: the naive Bayes classifier [20] and the Expectation Maximization (EM) algorithm [7] The naive Bayes algorithm is one of a class of statistical text classifiers that uses word frequencies as features. Other examples include TFIDF Rocchio [35, 34] regression models [38], k nearest neighbor [39] and Support Vector Machines [14] EM is a class of iterative algorithms for maximum likelihood estimation in problems with incomplete data. The result of combining these two is an algorithm that extends conventional text learning algorithms by using EM to dynamically ....

Yiming Yang and Christopher G. Chute. An application of least squares fit mapping to text information retrieval. In Proceedings of the Sixteenth Annual International ACM SIGIR Conference, 1993.

....A number of other researchers are using related linear algebra methods for information retrieval and classification work. Schutze [26] and Gallant [13] have used SVD and related dimension reduction ideas for word sense disambiguation and information retrieval work. Hull [16] and Yang and Chute [28] have used LSI SVD as the first step in conjunction with statistical classification (e.g. discriminant analysis) Using the LSI derived dimensions effectively reduces the number of predictor variables for classification. Wu et al. in [27] also used LSI SVD to reduce Using Linear Algebra for ....

Y. Yang and C. G. Chute, An application of least squares fit mapping to text information retrieval, in Proceedings of the Sixteenth Annual International ACM-SIGIR Conference, 1993, pp. 281--290.

....serious in categorization than in retrieval. That is, the vocabulary gap between free texts and the controlled indexing language of a particular database is usually large. Consequently, search methods based on shared words between free text and category names typically exhibit poor performance [1] [2] [3] The importance of using human knowledge to solve the vocabulary gap problem has been recognized, and statistical learning of text to categories mapping based on human assignments has been a major focus in recent research in text categorization [3] 4] 5] 6] 7] The LLSF mapping is a ....

.... and statistical learning of text to categories mapping based on human assignments has been a major focus in recent research in text categorization [3] 4] 5] 6] 7] The LLSF mapping is a successful learner relying on past human relevance judgments, and can be used for both text retrieval [2] and text categorization. Significant improvements of LLSF mapping have been observed in previous evaluations, compared to alternatives such as word based matching methods which do not use any human knowledge, and thesaurus based methods which are heavily dependenton manually coded human knowledge ....

Yang Y, Chute CG. (1993, July) An application of Least Squares Fit Mapping to text information retrieval. Proc 16th Ann Int ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 93), 281-290.

No context found.

Y. Yang and C. G. Chute. An application of Least Squares Fit mapping to text information retrieval. In R. Korfhage, E. Rasmussen, and P. Willett, editors, Development in Information Retrieval, pages 281--290, Pittsburgh, US, 1993. ACM Press, New York, US. An extended version appears as [139].

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC