Comparison between error correcting output codes and fuzzy support vector machines
نویسندگان
چکیده
منابع مشابه
Error Correcting Output Codes vs. Fuzzy Support Vector Machines
Error correcting output codes (ECOC) have been proposed to enhance generalization ability of classifiers. If, instead of discrete error functions, continuous error functions are used, unclassifiable regions of multiclass support vector machines are resolved. In this paper, we discuss minimum operations as well as average operations for error functions of support vector machines and show the equ...
متن کاملQuestion Classification with Support Vector Machines and Error Correcting Codes
In this paper we consider a machine learning technique for question classification. The goal is to replace our regular expression based classifier with a classifier that learns from a set of labeled questions. We have realized that an enourmous amount of time is required to create a rich collection of patterns and keywords for a good coverage of questions in an opendomain application. We decide...
متن کاملOn Nearest-Neighbor Error-Correcting Output Codes with Application to All-Pairs Multiclass Support Vector Machines
A common way of constructing a multiclass classifier is by combining the outputs of several binary ones, according to an error-correcting output code (ECOC) scheme. The combination is typically done via a simple nearest-neighbor rule that finds the class that is closest in some sense to the outputs of the binary classifiers. For these nearest-neighbor ECOCs, we improve existing bounds on the er...
متن کاملOnline error correcting output codes
This article proposes a general extension of the error correcting output codes framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. In particular, this extension supports the use of both online example incremental and batch classifiers as base learners. The extension of the traditional problem...
متن کاملSensitive Error Correcting Output Codes
We present a reduction from cost sensitive classi cation to binary classi cation based on (a modi cation of) error correcting output codes. The reduction satis es the property that regret for binary classi cation implies l2-regret of at most 2 for cost-estimation. This has several implications: 1) Any regret-minimizing online algorithm for 0/1 loss is (via the reduction) a regret-minimizing onl...
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: Pattern Recognition Letters
سال: 2005
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2005.03.014