Bayes’ rule specifies how to obtain a posterior from a class of hypotheses endowed with a prior and the observed data. There are three fundamental ways to use this posterior for predicting the future: marginalization (integration over the hypotheses w.r.t. the posterior), MAP (taking the a posteriori most probable hypothesis), and stochastic model selection (selecting a hypothesis at random acc...

In this paper we present a probabilistic framework for case-based reasoning in data-intensive domains, where only weak prior knowledge is available. In such a probabilistic viewpoint the attributes are interpreted as random variables, and the case base is used to approximate the underlying joint probability distribution of the attributes. Consequently structural case adaptation (and parameter a...

Regression testing is an important but expensive software maintenance activity performed with the aim of providing confidence in modified software. Regression test selection techniques reduce the cost of regression testing by selecting test cases for a modified program from a previously existing test suite. Many researchers have addressed the regression test selection problem for procedural lan...

Hydrological model development and application for research projects is most often a cycle of model selection, model application, model adaptation and enhancement. The reason for the adaptation of an existing one or the development of a new model is that most of the conceptual hydrological models have been developed for a specific test catchment, scale and problem focus. The transfer to other c...

We derive new margin-based inequalities for the probability of error of classifiers. The main feature of these bounds is that they can be calculated using the training data and therefore may be effectively used for model selection purposes. In particular, the bounds involve empirical complexities measured on the training data (such as the empirical fatshattering dimension) as opposed to their w...

With pairwise testing, the test model is a list of N parameters. Each test case is an N -tuple; the test space is the cross product of the N parameters. A pairwise test is a set of N -tuples where every pairwise combination of the parameter values is contained in at least one of the N -tuples. Well-known algorithms generate pairwise test sets far smaller than the test space. Pairwise testing ha...

Abstract: Transmission lines are used to transmit electric power for large distances in electrical power systems. The rapid growth of electrical power systems over the past few decades has resulted in a massive increase of the quantity of lines operative and their total length. These lines are exposed to faults as a result of lightning, short circuits, faulty equipment‟s &human errors. This pap...

This paper presents a navigation component based on a hybrid case-based reasoning (CBR) and reinforcement learning (RL) approach for an AI agent in a real-time strategy (RTS) game. Spatial environment information is abstracted into a number of influence maps. These influence maps are then combined into cases that are managed by the CBR component. RL is used to update the case solutions which ar...

Project portfolio selection is very important subject of decision-makers in project-based organizations. The best assignment of resources to the most appropriate projects is necessary as financing projects with low benefit is just waste of organization's resources. However, existing project selection models pay not much attention the structure and special features of projects as a selection cri...