Probabilistic outputs for a new multi-class Support Vector Machine
ثبت نشده
چکیده
Support Vector Machines are learning paradigm originally developed on the basis of a binary classification problem with signed outputs ±1. The aim of this work is to give a probabilistic interpretation to the numerical output values into a multi-classification learning problem framework. For this purpose, a recent SV Machine, called `-SVCR, addressed to avoid the lose of information occurred in the usual 1-v-1 training, is implemented. On this structure, a certain class of probabilistic outputs are considered in an ensemble architecture with learning machines working in parallel. New architecture allows to define a ‘interpretation’ map working on signed and probabilistic outputs improving user’s control on the classification problem.
منابع مشابه
Fault diagnosis in a distillation column using a support vector machine based classifier
Fault diagnosis has always been an essential aspect of control system design. This is necessary due to the growing demand for increased performance and safety of industrial systems is discussed. Support vector machine classifier is a new technique based on statistical learning theory and is designed to reduce structural bias. Support vector machine classification in many applications in v...
متن کاملA Probabilistic Tri-class Support Vector Machine
A probabilistic interpretation for the output obtained from a tri-class Support Vector Machine into a multi-classification problem is presented in this paper. Probabilistic outputs are defined when solving a multi-class problem by using an ensemble architecture with tri-class learning machines working in parallel. This architecture enables the definition of an ‘interpretation’ mapping which wor...
متن کاملSupport vector regression with random output variable and probabilistic constraints
Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM). In this paper, a new model of SVR with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. Using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...
متن کاملFeature Selection Using Multi Objective Genetic Algorithm with Support Vector Machine
Different approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods. In filter methods, features subsets are selected due to some measu...
متن کاملProbabilistic support vector machines for multi-class alcohol identification
In this work we address the use of support vector machines in multi-category problems. In our case, the objective is to classify eight different inds of alcohols with just one SnO2 sensor using thermomodulation. The use of support vector machines in the field of sensors signals recognition s beginning to be used due to the ability to generalize in a binary classification problem with a small nu...
متن کامل