For the problem of dividing the space originally pad tionec ~y a blurred boundary, every learning algorithm can make the probability of incorrect prediction of an individual example E decrease with the number of training examples t. We address here the question of how the asymptotic form of ~ ( t ) as well as its limit of convergence reflect the choice of learning algorithms. The error minimum ...

Previous work has employed an approach to the evaluation of wrapper feature selection methods that may overstate their ability to improve classification accuracy, because of a phenomenon akin to overfitting. This paper discusses this phenomenon in the context of recent work in machine learning, demonstrates that previous work in MIR has indeed exaggerated the efficacy of feature selection for m...

We propose a linearly penalized support vector machines (LP-SVM) model for feature selection. Its application to a problem of customer retention and a comparison with other feature selection techniques underlines its effectiveness.

Purpose The purpose of this study is to investigate whether machine learning with dosiomic, radiomic, and demographic features allows for xerostomia risk assessment more precise than normal tissue complication probability (NTCP) models based on the mean radiation dose to parotid glands. Material and methods A cohort of 153 head-and-neck cancer patients was used to model xerostomia at 0-6 mont...

We consider the following sample selection problem. We observe in an online fashion a sequence of samples, each endowed by a quality. Our goal is to either select or reject each sample, so as to maximize the aggregate quality of the subsample selected so far. There is a natural trade-off here between the rate of selection and the aggregate quality of the subsample. We show that for a number of ...

BACKGROUND Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally ...

The effectiveness of ten different feature sets in classification of voice recordings of the sustained phonation of the vowel sound /a/ into a healthy and pathological classes is investigated as well as a new approach to building a sequential committee of support vector machines (SVM) for the classification is proposed. The optimal values of hyper-parameters of the committee and the feature set...

In this paper, we propose a fully automated method to individually classify patients with Alzheimer’s disease (AD) and elderly control subjects based on anatomical magnetic resonance imaging (MRI). Our approach relies on the identification of gray matter (GM) atrophy patterns using whole-brain parcellation into anatomical regions and the extraction of GM characteristics in these regions. Discri...

the Machine learning field has many applications in almost every area. In this digitized world, everything has to be automated to improve quality, time complexity, accuracy etc. In the biomedical area, information is mainly in natural language text format. The biomedical researchers need fast information accessing tools for extracting useful information from huge amount of biomedical repositori...

this paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (lssvm). two wavelet selection criteria maximum energy to shannon entropy ratio and maximum relative wavelet energy are used and compared to select an appropriate wavelet for feature extraction. the fault diagnosis method co...