Semi-supervised is the machine learning field. In the previous work, selection of pairwise constraints for semi-supervised clustering is resolved using active learning method in an iterative manner. Semi-supervised clustering derived from the pairwise constraints. The pairwise constraint depends on the two kinds of constraints such as must-link and cannot-link.In this system, enhanced iterative...

Semi-supervised is the machine learning field. In the previous work, selection of pairwise constraints for semi-supervised clustering is resolved using active learning method in an iterative manner. Semi-supervised clustering derived from the pairwise constraints. The pairwise constraint depends on the two kinds of constraints such as must-link and cannot-link.In this system, enhanced iterative...

An iterative learning identiication method is proposed for curve identiication problems. The basic idea is to convert the curve identiication problem into an optimal tracking control problem. The measured trajectories are regarded as the desired trajectories to be optimally tracked and the curve to be identiied is taken as a virtual control function. A high-order learning updating law is applie...

In this paper an iterative learning control design method is depicted, leading to a feedforward controller minimizing tracking error of repetitive trajectories. The approach is extended to the case of a fuzzy controller, where the plant inverse is approximated by a fuzzy system. This provides some extra features, being also suitable to be applied to nonlinear plants. A simple application illust...

-This paper describes a learning rule of neural networks via a simultaneous perturbation and an analog feedforward neural network circuit using the learning rule. The learning rule used here is a stochastic gradient-like algorithm via a simultaneous perturbation. The learning rule requires only forward operations o f the neural network. Therefore, it is suitable for hardware implementation. Fir...

In this paper, we provide a brief summary of elementary research in rule learning. The two main research directions are descriptive rule learning, with the goal of discovering regularities that hold in parts of the given dataset, and predictive rule learning, which aims at generalizing the given dataset so that predictions on new data can be made. We briefly review key learning tasks such as as...

This paper addresses the possibility of capacity withholding by energy producers, who seek to increase the market price and their own profits. The energy market is simulated as an iterative game, where each state game corresponds to an hourly energy auction with uniform pricing mechanism. The producers are modeled as agents that interact with their environment through reinforcement learning (RL...

This paper presents a novel competitive neural network learning approach to schedule requests to a cluster of Web servers. Traditionally, the scheduling algorithms for distributed systems are not applicable to control Web server clusters because different client domains have different Web traffic characteristics, Web workload is highly variable, and Web requests show a high degree of self-simil...

This paper describes methods for reasoning with missing, irrelevant and not applicable meta-values in the AQ attributional rule learning. The methods address issues of handling these values in datasets both for rule learning and rule testing. In rule learning, the presence of these values affects the extension-against generalization operator in star generation, and the rule matching operator. I...