Weight-Based Feature Selection for Conditional Maximum Entropy Models
نویسندگان
چکیده
منابع مشابه
Conditional-Entropy Metrics for Feature Selection
We examine the task of feature selection, which is a method of forming simplified descriptions of complex data for use in probabilistic classifiers. Feature selection typically requires a numerical measure or metric of the desirability of a given set of features. The thesis considers a number of existing metrics, with particular attention to those based on entropy and other quantities derived f...
متن کاملMixtures of Conditional Maximum Entropy Models
Driven by successes in several application areas, maximum entropy modeling has recently gained considerable popularity. We generalize the standard maximum entropy formulation of classification problems to better handle the case where complex data distributions arise from a mixture of simpler underlying (latent) distributions. We develop a theoretical framework for characterizing data as a mixtu...
متن کاملA Fast Algorithm for Feature Selection in Conditional Maximum Entropy Modeling
This paper describes a fast algorithm that selects features for conditional maximum entropy modeling. Berger et al. (1996) presents an incremental feature selection (IFS) algorithm, which computes the approximate gains for all candidate features at each selection stage, and is very time-consuming for any problems with large feature spaces. In this new algorithm, instead, we only compute the app...
متن کاملExtended MULTIMOORA method based on Shannon entropy weight for materials selection
Selection of appropriate material is a crucial step in engineering design and manufacturing process. Without a systematic technique, many useful engineering materials may be ignored for selection. The category of multiple attribute decision-making (MADM) methods is an effective set of structured techniques. Having uncomplicated assumptions and mathematics, the MULTIMOORA method as an MADM appro...
متن کاملEfficient Large-Scale Distributed Training of Conditional Maximum Entropy Models
Training conditional maximum entropy models on massive data sets requires significant computational resources. We examine three common distributed training methods for conditional maxent: a distributed gradient computation method, a majority vote method, and a mixture weight method. We analyze and compare the CPU and network time complexity of each of these methods and present a theoretical ana...
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: Information Technology Journal
سال: 2009
ISSN: 1812-5638
DOI: 10.3923/itj.2009.764.769