Multiclass Learning at One-class Complexity
نویسندگان
چکیده
We show in this paper the multiclass classification problem can be implemented in the maximum margin framework with the complexity of one binary Support Vector Machine. We show reducing the complexity does not involve diminishing performance but in some cases this approach can improve the classification accuracy. The multiclass classification is realized in the framework where the output labels are vector valued.
منابع مشابه
Optimal learners for multiclass problems
The fundamental theorem of statistical learning states that for binary classification problems, any Empirical Risk Minimization (ERM) learning rule has close to optimal sample complexity. In this paper we seek for a generic optimal learner for multiclass prediction. We start by proving a surprising result: a generic optimal multiclass learner must be improper, namely, it must have the ability t...
متن کاملEfficient Multiclass Boosting Classification with Active Learning
We propose a novel multiclass classification algorithm Gentle Adaptive Multiclass Boosting Learning (GAMBLE). The algorithm naturally extends the two class Gentle AdaBoost algorithm to multiclass classification by using the multiclass exponential loss and the multiclass response encoding scheme. Unlike other multiclass algorithms which reduce the K-class classification task to K binary classifi...
متن کاملEfficient Approach Multiclass SVM For Vowels Recognition
In this paper we present and investigate the performance of a simple framework for multiclass problems of support vector machine (SVM), we present a new approach named EAMSVM (Efficient Approach Multiclass SVM), in order to achieve high classification efficiency for multiclass problems. The proposed paradigm builds a binary tree for multiclass SVM by genetic algorithms with the aim of obtaining...
متن کاملAutomatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification
An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies or...
متن کاملLearning Kernels Using Local Rademacher Complexity
We use the notion of local Rademacher complexity to design new algorithms for learning kernels. Our algorithms thereby benefit from the sharper learning bounds based on that notion which, under certain general conditions, guarantee a faster convergence rate. We devise two new learning kernel algorithms: one based on a convex optimization problem for which we give an efficient solution using exi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005