We investigate the label complexity of active learning under some smoothness assumptions on the data-generating process. We propose a procedure, PLAL, for “activising” passive, sample-based learners. The procedure takes an unlabeled sample, queries the labels of some of its members, and outputs a full labeling of that sample. Assuming the data satisfies “Probabilistic Lipschitzness”, a notion o...

We propose a novel model-based hearing compensation strategy and gradient-free optimization procedure for a learning-based hearing aid design. Motivated by physiological data and normal and impaired auditory nerve models, a hearing compensation strategy is cast as a neural coding problem, and a Neurocompensator is designed to compensate for the hearing loss and enhance the speech. With the goal...

Rumelhart, Hinton and Williams [Rumelhart et al. 86] describe a learning procedure for layered networks of deterministic, neuron-like units. This paper describes further research on the learning procedure. We start by describing the units, the way they are connected, the learning procedure, and the extension to iterative nets. We then give an example in which a network learns a set of filters t...

This paper proposes an adaptive method to construct a fuzzy rule-based classification system with high performance for pattern classification problems. The proposed method consists of two procedures: an error correction-based learning procedure and an additional learning procedure. The error correction-based learning procedure adjusts the grade of certainty of each fuzzy rule by its classificat...

The classical supervised pattern recognition problem is considered. A model of learning Boolean formulas is implemented. A new computational procedure for constructing quantitative non-reducible descriptors is suggested. The proposed procedure has a diminished number of computational operations with respect to the procedure for constructing non-reducible descriptors and allows incremental learn...

In this study, the role of selective attention in implicit artificial grammar (AG) learning was investigated using the overlapping procedure; this novel procedure has been invented in order to exclude the methodological problems in the dual-task procedure. The results reveal that selective attention plays an important role in AG learning, although a possibility still remains that participants c...