A Fully Bayesian Sparse Probit Model for 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 کاملSparse representations for text categorization
Sparse representations (SRs) are often used to characterize a test signal using few support training examples, and allow the number of supports to be adapted to the specific signal being categorized. Given the good performance of SRs compared to other classifiers for both image classification and phonetic classification, in this paper, we extended the use of SRs for text classification, a metho...
متن کاملBayesian Text Categorization
Natural language processing is an interdisciplinary field of research which studies the problems and possibilities of automated generation and understanding of natural human languages. Text categorization is a central subfield of natural language processing. Automatically assigning categories to digital texts has a wide range of applications in today’s information society—from filtering spam to...
متن کاملSparse Logistic Regression for Text Categorization
This paper studies regularized logistic regression and its application to text categorization. In particular we examine a Bayesian approach, lasso logistic regression, that simultaneously selects variables and provides regularization. We present an efficient training algorithm for this approach, and show that the resulting classifiers are both compact and have state-of-the-art effectiveness on ...
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: Open Journal of Statistics
سال: 2014
ISSN: 2161-718X,2161-7198
DOI: 10.4236/ojs.2014.48057