In this paper we describe a divide-and-combine strategy for decomposition of a complex prediction problem into simpler local sub-problems. We rstly show how to perform a soft decomposition via clustering of input data. Such decomposition leads to a partition of the input space into several regions which may overlap. Therefore, to each region is assigned a local predictor (or expert) which is tr...

Human resource studies seeks to investigate the factors affecting training and the impact of these trainings on the development of human learning in organizations, and in this field, it uses different methods. The complexity of the educational environment and others, the different dimensions of human resources training and the different conditions and influencing factors based on blended learni...

To improve teachers’ productivity, factors involved need to be identified and strengthened. To this end a conceptual model was initially constructed and then modified based on the feedback given by 12 experts. The final model consisted of three dimensions or topics, four sub topics, 22 concepts, or constituents, and 79 indices. The input dimension included subtopics of cognitive and behavioral ...

Experts, such as scientists, teachers, and engineers for example, bring to management positions a strong personal background of training and experience that is often lacking in specific management expertise, yet they are apparently successful in their new roles. Are the decision making skills that are developed during the acquisition of their expertise the key to their success? The discussion w...

Efficient parallel learning algorithms are proposed for training a powerful modular neural network, the hierarchical mixture of experts (HME). Parallelizations are based on the concept of modular parallelism, i.e. parallel execution of network modules. From modeling the speedup as a function of the number of processors and the number of training examples, several improvements are derived, such ...

Ensemble learning algorithms aim to train a group of classifiers to enhance the generalization ability. However, vast of those algorithms are learning in batches and the base classifiers (e.g number, type) must be predetermined. In this paper, we propose an ensemble algorithm called INLEX (Incremental Network with Local EXperts ensemble) to learn suitable number of linear classifiers in an onli...

The mixture-of-experts (ME) models can be useful to solve complicated classification problems in real world. However, in order to train the ME model with not only labeled data but also unlabeled data which are easier to come, a new learning algorithm that considers characteristics of the ME model is required. We proposed global-local co-training (GLCT), the hybrid training method of the ME mode...

We present the evaluation of an integrated authoring tool for rapid prototyping of dialogue systems. These dialogue systems are designed to support virtual humans engaging in advanced question-answering dialogues, such as for training tactical questioning skills. The tool was designed to help non-experts, who may have little or no knowledge of linguistics or computer science, build virtual char...