Zero temperature Gibbs learning is considered for a connected committee machine with K hidden units. For large K, the scale of the learning curve strongly depends on the target rule. When learning a perceptron, the sample size P needed for optimal generalization scales so that N P KN, where N is the dimension of the input. This even holds for a noisy perceptron rule if a new input is classiied ...

In recent years, brain-computer interface (BCI) technology has emerged very rapidly. Brain-computer interfaces (BCIs) bring us a new communication interface technology which can translate brain activities into control signals of devices like computers, robots. The preprocessing of electroencephalographic (EEG) signal and translation algorithms play an important role in EEG-based BCIs. In this s...

One approach in classification tasks is to use machine learning techniques to derive classifiers using learning instances. The cooperation of several base classifiers as a decision committee has succeeded to reduce classification error. The main current decision committee learning approaches boosting and bagging use resampling with the training set and they can be used with different machine le...

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We present a method for determining the globally optimal on-line learning rule for a soft committee machine under a statistical mechanics framework. This work complements previous results on locally optimal rules, where only the rate of change in generalization error was considered. We maximize the total reduction in generalization error over the whole learning process and show how the resultin...

In many data mining applications we are given a set of training examples and asked to construct a regression machine or a classifier that has low prediction error or low error rate on new examples. An important issue is speed especially when there are large amounts of data. We show how both classification accuracy and prediction error can be reduced by using boosting techniques to implement com...