An Unconstrained Handwritten Numerals Recognition Way Based on Combined Classifier;

In order to improve the performance of the minority class, a combined classifier algorithm is presented based on data pre- processing.

The characteristics of rough set theory and neural networks and the compementarity of these two theories are studied firstly in this paper,then it puts forward a method of constructing an combined classifier based on rough set theory(C3RST).

Application of combining classifier based on AdaBoost to remote sensing classification;

Aiming at the problem of radar radiating source recognition system, we put forward a scheme applying combining classifier to radiating source recognition.

Finally,a combinatorial classifier is designed to recognize human faces.

Based on extracting prosody and voice quality features from emotional speech, three kinds of classifiers including Support Vector Machines(SVM), K-Nearest Neighborhood(KNN) and Artificial Neural Network(ANN) are utilized to vote and construct an ensemble classifier so as to realize recognizing the four main speech emotion in Chinese like anger, happiness, sa.

Based on the analysis of traditional serial and parallel combination method, this paper presents a kind of new classifier combination based on hierarchical method.

In this paper, a new approach to segmenting flame images in kiln based on multiple BP network classifier combination is proposed.

A neural network classifier combination method is introduced in this paper and applied to handwritten numeral recognition.

Studied in this paper are the architectures and methods for combination of multiple classifiers in support vector machine(SVM) ensemble for multi-class classification.