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Medical thermography has been demonstrated an effective and inexpensive method for detecting breast cancer, in particular for tumors in early stages and in dense tissue. Image features can be extracted from breast thermo grams and used in a pattern classification stage for automated diagnosis and hence as a second objective opinion or for screening purposes. One of the main challenges for applying...
SVMs with the general purpose RBF kernel are widely considered as state-of-the-art supervised learning algorithms due to their effectiveness and versatility. However, in practice, SVMs often require more training data than readily available. Prior-knowledge may be available to compensate this shortcoming provided such knowledge can be effectively passed on to SVMs. In this paper, we propose a method...
In this article we proposed a feature selection method based on mutual information (MI) and intrinsic dimensionality (ID) estimators. First, MI ranks the normalized feature space in accordance to minimal-redundancy-maximal-relevance (mRMR) criterion. Next, ID estimates the minimum number of features to represent the observed properties of the data. Two techniques of ID were tested: principal component...
Early work has demonstrated that conserve self pattern recognition algorithm (CSPRA) produces promising performance in the field of anomaly detection. This paper further extends the applications of CSPRA to Fisher's Iris data, Indian Telugu data and Wisconsin breast cancer data. A formal description of the differences between the two detection strategies (classical CSPRA and selective CSPRA) is given...
Support vector machine is gaining popularity due to many attractive features and promising empirical performance in the fields of nonlinear and high dimensional pattern recognition. TSVM (transductive support vector machine) takes into account a particular test set and tries to minimize misclassifications of just those particular examples. PTSVM (progressive transductive support vector machine) can...
When solving the problem in computer assisted detection by the approach of pattern recognition, the lesion data always exhibited high-dimensional and inhomogeneous, which makes most of the traditional classifiers can not performance very well. In this paper, a novel approach based on the dynamic feature subset selection and the EM algorithm with Naive Bayesian classifier integration algorithm (DSFS+EMNB)...
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