[PDF] Top 20 Effect of small sample size on text categorization with support vector machines

... The second dataset was created by Lewis and Ringuette (1994) and is the one most commonly used to benchmark text classification algorithms. The collection is composed of 21,578 short news stories from the Reuters ... See full document

... John Wilbur Effect of small sample size on text categorization with support vector machines Pawel Matykiewicz and John Pestian PubAnnotation - a persistent and sharable corpus and annota[r] ... See full document

... texts categorization is an important component in many information organization and management ...for text categorization in terms of learning speed, real-time classification speed, and ... See full document

... Let there be a training set of size n. With the help of bagging, we generate m new training sets of size n ′ each. A uniform replacement sampling is done with around (1 - 1/e) fraction of uniqueness. It ... See full document

... and sample screening methods ...has small-scale problems to solve in the late stage of the training ...problems’ size can be large in the early stage of the training ... See full document

... large size of training data for the HMM tagger and it performs not very well for the small set of training data since the bigram probabilities are not found sufficiently in the case of small ... See full document

... Fuzzy Support Vector Machines (PSOFuzzySVM) to predict oil well gas lift performance and production optimization in a ...tubing size and choke ...decided effect on the efficiency and ... See full document

... constructing text classification rules can overcome all these ...relatively small number of manually pre-classified training documents, the problem of learning text classification rules can be cast ... See full document

... the effect of the polynomial degree p on the texture classification performance, a set of experiments was performed with varying degrees of ...window size was fixed at 17 ... See full document

... in text such as proper names, biological species, and temporal expressions into some predefined ...Nepali text, based on the Support Vector Machine (SVM) is presented which is one of machine ... See full document

... Note that feature extraction methods also handle problems with synonymy, by grouping together words that mean similar things, or by restructuring the data (i.e. the number of fea- tures) according to a small ... See full document

... years, Support Vector Machines (SVMs) have demonstrated excellent functioning in a variety of area categorization ...areas categorization by operating ...in categorization of ... See full document

... of support vector machine comes from statistical learning ...the support vector set (typically is 3%— 5% of the former) and the classification decision function is got by ... See full document

... We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization perfor- mance even with input data of high dimensional feature spaces. ... See full document

... Given a set B of biological sequences, each assigned to a taxon (training set), a classifier is trained with these sequences in order to compute a classification model that predicts the taxon of each sequence from the ... See full document

... This work is organized as follows: In Section 2 we introduce all basic and advanced notions and discuss the latter. We then explain the main ideas of this work and informally present some auxiliary results such as the ... See full document

... We need to point out that there are several different definitions of robustness in the literature. In this paper, as well as the aforementioned robust classification papers, robustness is mainly under- stood from a ... See full document

... using Support Vector Machine classifier and Particle Swarm Optimization (Basari, et ...classifier, Support Vector Machines and Character- Based N-gram model (Ye, Zhang, & Law, ... See full document

... A few of these are mentioned below. The PCA technique was produced in 1991 [Turk and Pentland, 1991].PCA is one of the well-known systems utilized for feature extraction and data representation. It reduces the picture's ... See full document

... We have presented a new formula for bounding the covering numbers of SV machines in terms of the eigenvalues of an inte- gral operator induced by the kernel. We showed, by way of an example using a Gaussian ... See full document