The utility problem occurs when the cost associated with searching for relevant knowledge outweighs the benefit of applying this knowledge. One common machine learning strategy for coping with this problem ensures that stored knowledge is genuinely useful, deleting any structures that do not contribute to performance in a positive sense, and essentially limiting the size of the knowledge-base. ...

This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As ba...

End Stage Renal Disease is a severe chronic condition that corresponds to the final stage of kidney failure. Hemodialysis (HD) is the most widely used treatment method for ESRD. The HD treatment is costly and demanding from an organizational viewpoint, requiring day hospital beds, specialized nurses and periodical visits and exams of outpatients. In order to assess the performance of HD centers...

Although several researchers have integrated methods for reinforcement learning (RL) with case-based reasoning (CBR) to model continuous action spaces, existing integrations typically employ discrete approximations of these models. This limits the set of actions that can be modeled, and may lead to non-optimal solutions. We introduce the Continuous Action and State Space Learner (CASSL), an int...

Whenever that a musician plays a musical piece, the result is never a literal interpretation of the score. These performance deviations are intentional and constitute the essence of the musical communication. Deviations are usually thought of as conveying expressiveness. Two main purposes of musical expression are generally recognized: the clarification of the the musical structure and the tran...

This paper proposes a methodology of maintaining Case Based Reasoning (CBR) systems by using fuzzy decision tree induction a machine learning technique. The methodology is mainly based on the idea that a large case library can be transformed to a small case library together with a group of adaptation rules, which are generated by fuzzy decision trees. Firstly, an approach to learning feature we...

In this paper, we discuss a method to nd a critical casebase to classify boolean concepts. By a critical casebase we mean that any subset of the casebase cannot represent a concept correctly. This notion is important for reduction of not only the size of casebase but also classi cation speed. In this paper, we consider set-inclusion based similarity which is originated from a legal reasoning sy...

We propose the implementation of an intelligent information system on free and open source software. This system will consist of a case-based reasoning (CBR) system and several machine learning modules to maintain the knowledge base and train the CBR system thus enhancing its performance. Our knowledge base will include data on free and open source software provided by the Debian project, the F...

The problem of heterogeneous case representation poses a major obstacle to realising real-life multi-case-base reasoning (MCBR) systems. The knowledge overhead in developing and maintaining translation protocols between distributed case bases poses a serious challenge to CBR developers. In this paper, we situate CBR as a flexible problemsolving strategy that relies on several heterogeneous know...

CBR is one of the techniques that can be applied to the task of approximating a function over high-dimensional, continuous spaces. In Reinforcement Learning systems a learning agent is faced with the problem of assessing the desirability of the state it finds itself in. If the state space is very large and/or continuous the availability of a suitable mechanism to approximate a value function – ...