Fuzzy cognitive map (FCM) is a universal tool for modeling dynamic decision support systems. It can be constructed by the experts or learned based on data. FCM models learned from data are denser than those created by experts. We developed an evolutionary learning approach for fuzzy cognitive maps based on density and system performance indicators. It allows to select only the most significant ...

We present a new approach for learning compact and intuitive distributed representations with binary encoding. Rather than summing up expert votes as in products of experts, we employ for each variable the opinion of the most reliable expert. Data points are hence explained through a partitioning of the variables into expert supports. The partitions are dynamically adapted based on which expert...

Probabilistic algorithms o er a means of computing that works with the grain of analogue hardware, rather than against it. This paper proposes the use of such an algorithm in applications where the advantages of analogue hardware are most likely to be realised.

Learning how to refer effectively in an expert-referral network is an emerging challenge at the intersection of Active Learning and MultiAgent Reinforcement Learning. Distributed interval estimation learning (DIEL) was previously found to be promising for learning appropriate referral choices, compared to greedy and Q-learning methods. This paper extends these results in several directions: Fir...

background : learning in a clinical environment is an inseparable part of a training program in medical education. to evaluate the quality of training in a clinical environment, a comprehensive questionnaire which is adjusted for local purposes is essential. this study was conducted to determine the validity and reliability of activities coaching context (acc)-questionnaire from the perspective...

When labeled examples are not readily available, active learning and transfer learning are separate efforts to obtain labeled examples for inductive learning. Active learning asks domain experts to label a small set of examples, but there is a cost incurred for each answer. While transfer learning could borrow labeled examples from a different domain without incurring any labeling cost, there i...