In the present paper, we study iterative learning of indexable concept classes from noisy data. We distinguish between learning from positive data only and learning from positive and negative data; synonymously, learning from text and informant, respectively. Following 20], a noisy text (a noisy informant) for some target concept contains every correct data item innnitely often while in additio...

Important refinements of concept learning in the limit from positive data considerably restricting the accessibility of input data are studied. Let c be any concept; every infinite sequence of elements exhausting c is called positive presentation of c. In all learning models considered the learning machine computes a sequence of hypotheses about the target concept from a positive presentation o...

This paper presents a novel approach to the unsupervised learning of syntactic analyses of natural language text. Most previous work has focused on maximizing likelihood according to generative PCFG models. In contrast, we employ a simpler probabilistic model over trees based directly on constituent identity and linear context, and use an EM-like iterative procedure to induce structure. This me...

As we aim to control complex systems, use of a simulator in model-based reinforcement learning is becoming more common. However, it has been challenging to overcome the Reality Gap, which comes from nonlinear model bias and susceptibility to disturbance. To address these problems, we propose a novel algorithm that combines data-driven system identification approach (Gaussian Process) with a Dif...

Within the present paper, we investigate the principal learning capabilities of iterative learners in some more details. The general scenario of iterative learning is as follows. An iterative learner successively takes as input one element of a text (an informant) of a target concept as well as its previously made hypothesis, and outputs a new hypothesis about the target concept. The sequence o...

Substrate-handling robots for semiconductor and flat-panel-display manufacturing applications possess undesirable structural flexibilities and frictional effects which erode closed-loop stability and deteriorate tracking performance. In this study, advanced control and identification techniques are applied to cope with these control challenges. The techniques include sensorless modal stabilizat...

We propose an unsupervised approach to learning non-concatenative morphology, which we apply to induce a lexicon of Arabic roots and pattern templates. The approach is based on the idea that roots and patterns may be revealed through mutually recursive scoring based on hypothesized pattern and root frequencies. After a further iterative refinement stage, morphological analysis with the induced ...