Combining Classifiers for Quick Text Categorization
نویسندگان
چکیده
منابع مشابه
Sparse Bayesian Classifiers for Text Categorization (U)
(U) This paper empirically compares the performance of different Bayesian models for text categorization. In particular we examine so-called “sparse” Bayesian models that explicitly favor simplicity. We present empirical evidence that these models retain good predictive capabilities while offering significant computational advantages.
متن کاملSparse Bayesian Classifiers for Text Categorization
This paper empirically compares the performance of different Bayesian models for text categorization. In particular we examine so-called “sparse” Bayesian models that explicitly favor simplicity. We present empirical evidence that these models retain good predictive capabilities while offering significant computational advantages.
متن کاملCombining Multiple Classifiers Using Dempster's Rule of Combination for Text Categorization
In this paper, we present an investigation into the combination of four different classification methods for text categorization using Dempster's rule of combination. These methods include the Support Vector Machine, kNN (nearest neighbours), kNN model-based approach (kNNM), and Rocchio methods. We first present an approach for effectively combining the different classification methods. We then...
متن کاملCombining classifiers for flexible genre categorization of web pages
With the increase of the number of web pages, it is very difficult to find wanted information easily and quickly out of thousands of web pages retrieved by a search engine. To solve this problem, many researches propose to classify documents according to their genre, which is another criteria to classify documents different from the topic. Most of these works assign a document to only one genre...
متن کاملCombining LSI with other Classifiers to Improve Accuracy of Single-label Text Categorization
This paper describes the combination of k-NN and SVM with LSI to improve their performance in single-label text categorization tasks, and the experiments performed with six datasets to show that both k-NN-LSI (the combination of k-NN with LSI) and SVM-LSI (the combination of SVM with LSI) outperform the original methods for a significant fraction of the datasets. Overall, both combinations pres...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Energy Procedia
سال: 2011
ISSN: 1876-6102
DOI: 10.1016/j.egypro.2011.11.125