Run off resulted from rainfall is the main way of receiving water in most parts of the World. Therefore, prediction of runoff volume resulted from rainfall is getting more and more important in control, harvesting and management of surface water. In this research a number of machine learning and data mining methods including support vector machines, regression trees (CART algorithm), model tree...

Abstract Minimizing bounds of leave-one-out (loo) errors is an important and efficient approach for support vector machine (SVM) model selection. Past research focuses on their use for classification but not regression. In this article, we derive various loo bounds for support vector regression (SVR) and discuss the difference from those for classification. Experiments demonstrate that the prop...

Most computational models of gender classification use global information (the full face image) giving equal weight to the whole face area irrespective of the importance of the internal features. Here we use a two-way representation of face images that includes both global and featural information. We use dimensionality reduction techniques and a support vector machine classifier and show that ...

The relevance vector machine (RVM) is applied for feature-vector quantization (codebook design) and for density-function estimation in high-dimensional feature space. The RVM represents a Bayesian extension of the widely applied support vector machine (SVM). The use of RVMs for quantization and density-function estimation is explored with application to discrete and continuous HMMs, respectivel...

New functionals for parameter (model) selection of Support Vector Machines are introduced based on the concepts of the span of support vectors and rescaling of the feature space. It is shown that using these functionals, one can both predict the best choice of parameters of the model and the relative quality of performance for any value of parameter.

ةـصلاخلا : ةيبرعلا ماقرلأا ىلع يئاقلتلا فرعتلل ةقيرط ةقرولا هذه فصت ) ةيدنهلا ( بساحلاب لاصتا نود ديلا طخب ةبوتكملا مادختساب تاهجتملا معد تانيآام ةيفخملا فوآرام جذامنو . ةعسومو ،ةطسوتمو ،ةيلحم سيياقملل تازيمم مادختسا متو . ىلإ لوصولا مت ةيلمعلا براجتلا للاخ نمو تاريغتملا ميق يتلا تاهجتملا معد تانيآامل يطعت ،لماشلا ثحبلل مزراوخ مادختسا للاخ نم فرعتلا تلادعم ىلعأ ةفاضإ مت كلذ ىلإ ت فينصتلا جئاتن...