This short contribution presents the first paper in which Vapnik and Chervonenkis describe the foundations of the Statistical Learning Theory [10]. The original paper was published in the Doklady, the Proceedings of the USSR Academy of Sciences, in 1968. An English translation was published the same year in Soviet Mathematics, a journal from the American Mathematical Society publishing translat...

This paper investigates detection of architectural distortion in mammographic images using support vector machine. Hausdorff dimension is used to characterise the texture feature of mammographic images. Support vector machine, a learning machine based on statistical learning theory, is trained through supervised learning to detect architectural distortion. Compared to the Radial Basis Function ...

My principal research interest is in statistical machine learning. One of the key tasks in machine learning is to develop and model efficient inference systems that are able to analyze data and make predictions. Looking at this problem from a statistical perspective has led to a lot of work on probabilistic inference systems. One such recent work is the development of graphical models, which fu...

Given a paremetric model, the task of statistical learning consists of a parameter learning part for determining unknown parameters and a model selection part for selecting an appropriate scale for a model that accommodates these parameters. Typically, the two tasks are implemented in a twophase procedure. First, a number of models of a same architecture but in different scales are enumerated, ...

bearing capacity prediction of axially loaded piles is one of the most important problems in geotechnical engineering practices, with a wide variety range of methods which have been introduced to forecast it accurately. machine learning methods have been reported by many contemporary researches with some degree of success in modeling geotechnical phenomena. in this study, a fairly new machine l...

Many scientists and researchers have been considering Support Vector Machines (SVMs) as one of the most powerful and robust algorithm in machine learning. For this reason, they have been used in many fields, such as pattern recognition, image processing, robotics, and many others. Since their appearance in 1995, from an idea of Vladimir Vapnik, bioinformatics community started to use this new t...