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The diversity of an ensemble is deemed to be a key factor which determines performance in ensemble learning. A variety of approaches have been advanced to quantify diversity by analyzing the prediction of classification which relies on the validation set. This paper proposes a new method how to measure diversity and ensemble for linear kernel Support Vector Machine, which is based on the characteristic...
Many machine learning technique have been employed for the classification of biological cells based on their Raman spectroscopy. Unfortunately, Raman spectroscopy data always has so many attributes that people who deal with them may often confront the problem of "curse of dimensionality". PCA is often used as a linear dimensionality reduction technique for preprocessing Raman data, which...
Aiming at the stability of Naive Bayes algorithm and overcoming the limitation of the attributes independence assumption in the Naive Bayes learning, we present an ensemble learning algorithm for naive Bayesian classifiers based on oracle selection (OSBE). Firstly we weaken the stability of the naive Bayes with oracle strategy, then select the better classifier as the component of ensemble of the...
Face recognition has become one of the latest research subjects of pattern recognition and image processing. Although many face recognition techniques have been proposed and many achievements have been obtained, we canpsilat get high recognition rate due to the changes of face expression, location, direction and light. In this paper we study human face recognition based on ensemble techniques. In...
Aiming at diversity being a necessary condition of the ensemble learning, we study method for improving diversity of the neural networks ensemble based on K-means clustering technique. In this paper, we propose a selecting approach that is first to train many classifiers through training set with neural network algorithm, and to classify data on validation set using classifiers. And then we use the...
The diversity of an ensemble of models is known to be an important factor in improving its generalization performance. We present an ensemble method based on clustering technique from libraries of models, EMC (ensemble of models based on clustering). It may be seen as a unified ensemble approach based on clustering. First, model libraries are generated using different learning algorithms and parameter...
Statistical learning theory is introduced to defect detection and a detection system of oil tube defect based upon support vector machine (SVM) is presented, it got the original information by multigroup vortex sensors and leakage magnetic sensors. The oil tube defect pattern had four class that is crack, etch pits, eccentric wear and unbroken, so the multi-classify support vector machine was adopt...
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