Cost-Sensitive Learning is a type of learning in data mining that takes the misclassification costs (and possibly other types of cost) into consideration. The goal of this type of learning is to minimize the total cost. The key difference between cost-sensitive learning and cost-insensitive learning is that cost-sensitive learning treats the different misclassifications differently. Costinsensi...

We evaluate the effect of automatically generated semantic clusters as as information source in our machine learning approach to the task of coreference resolution for Dutch. We compare these clusters which group semantically similar nouns together, to two semantic features based on WordNet encoding synonym and hypernym relations between nouns. Our experiments with two learners show that the cl...

We extend Q-learning to a noncooperative multiagent context, using the framework of generalsum stochastic games. A learning agent maintains Q-functions over joint actions, and performs updates based on assuming Nash equilibrium behavior over the current Q-values. This learning protocol provably converges given certain restrictions on the stage games (defined by Q-values) that arise during learn...

Explanation based learning produces generalized explanations from examples. These explanations are typically built in a deductive manner and they aim to capture the essential characteristics of the examples. Probabilistic explanation based learning extends this idea to probabilistic logic representations, which have recently become popular within the field of statistical relational learning. Th...

Coco (“cooperative/competitive”) values are a solution concept for two-player normalform games with transferable utility, when binding agreements and side payments between players are possible. In this paper, we show that coco values can also be defined for stochastic games and can be learned using a simple variant of Q-learning that is provably convergent. We provide a set of examples showing ...

We address the problem to tackle the very similar objects like “Chihuahua or muffin” problem to recognize at least in human vision level. Our regular deep structured machine learning still doesn’t solve it. We saw many times for about year in our community the problem. Today we proposed the state-of-the-art solution for it. Our approach is quite tricky to get the very high accuracy. We propose ...

In this article we approach one key aspect of the utility problem in explanationbased learning (EBL) | the expensive-rule problem | as an avoidable defect in the learning procedure. In particular, we examine the relationship between the cost of solving a problem without learning versus the cost of using a learned rule to provide the same solution, and refer to a learned rule as expensive if its...

Recent work in formal learning theory has attempted to capture the essentials of the conceptlearning task in a formal framework. This paper evaluates the potential contributions of certain kinds of models to the study of learning by exploring the philosophical implications of these models. Some of my remarks bear on mainstream AI learning techniques such as version spaces and explanation-based ...

OBJECTIVE To evaluate the academic satisfaction and importance among traditional learning (TL) and problem based learning (PBL) medical students, and to further evaluate the areas of concern in the academic education from the student's point of view. METHODS A cross sectional study was conducted at the College of Medicine, King Saud University, Riyadh, Kingdom of Saudi Arabia from May to June...