Hierarchical Bayesian inference in parameterised models offers an approach for controlling complexity. In this paper we utilise a novel prior for the leaning of a model’s structure. We call the prior node relevance determination. It is applicable in a range of models including sigmoid belief networks and Boltzmann machines. We demonstrate how the approach may be applied to determine structure i...

Many factors affect the success of Machine Learning (ML) on a given task. The representation and quality of the instance data is first and foremost. If there is much irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase is more difficult. It is well known that data preparation and filtering steps take considerable amount of...

My research work is centered around clustering, the most basic form of unsupervised learning. It extends to several closely related areas including: probabilistic mixture models, manifold leaning and dimension reduction, low rank approximation, maximum margin clustering and semi-supervised learning. An important theme of my work is to scale up the currently existing, but computationally demandi...

The red-black tree model for implementing balanced search trees, introduced by Guibas and Sedgewick thirty years ago, is now found throughout our computational infrastructure. Red-black trees are described in standard textbooks and are the underlying data structure for symbol-table implementations within C++, Java, Python, BSD Unix, and many other modern systems. However, many of these implemen...

This review paper presents the analysis about features of Learning Management System (LMS) and ways to include parental participation into LMS. A list 20 popular LMs given. The main LMS discussed. importance including parents Leaning Systems also

The article deals with the survey conducted upon basis of Pedagogical University students learning English as a foreign language. study is aimed at finding correlation between applying linguocultural approach and improving students’ leaning strategies implication. Thus, defined positive impact frequency usage by students. such types common, metacognitive, affective, social implemented grammar p...

We have created a radial basis function network that allocates a new computational unit whenever an unusual pattern is presented to the network. The network learns by allocating new units and adjusting the parameters of existing units. If the network performs poorly on a presented pattern, then a new unit is allocated which memorizes the response to the presented pattern. If the network perform...