Approximating distributions over strings is a hard learning problem. Typical GI techniques involve using finite state machines as models and attempting to learn both the structure and the weights, simultaneously. The PAutomaC competition is the first challenge to allow comparison between methods and algorithms and builds a first state of the art for these techniques. Both artificial data and re...

Raven and Song Scope are two, state-of-the-art automated sound analysis tools, based on machine learning techniques for detection of species vocalisations. Individually, these systems have been the subject of a number of reviews; however, to date there have been no comparisons made of their relative performance. This paper compares the tools based on six aspects: theory, software interface, eas...

We review a family of closely related query learning algorithms for unweighted and weighted tree automata, all of which are based on adaptations of the minimal adequate teacher (MAT) model by Angluin. Rather than presenting new results, the goal is to discuss these algorithms in sufficient detail to make their similarities and differences transparent to the reader interested in grammatical infe...

Many tasks in text and speech processing and computational biology require estimating functions mapping strings to real numbers. A broad class of such functions can be defined by weighted automata. Spectral methods based on the singular value decomposition of a Hankel matrix have been recently proposed for learning a probability distribution represented by a weighted automaton from a training s...

This paper studies the problem of learning weighted automata from a finite sample of strings with real-valued labels. We consider several hypothesis classes of weighted automata defined in terms of three different measures: the norm of an automaton’s weights, the norm of the function computed by an automaton, and the norm of the corresponding Hankel matrix. We present new data-dependent general...

An ad-hoc Learning Automata-based protocol for wireless LANs, capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by means of Learning Automata. The Learning Automaton takes into account the network feedback information in order to update the choice probability ...

The main objective of flow control in a store-and forward packet switched network is a good tradeoff between throughput and delay. The isarithmic method is an algorithm for network access level flow control [6], that allows packets enter the subnet only if a free "permit" exists at the source-node. A learning automaton is situated at each exit-node, attempting to make an optimal decision for th...

In this paper we have investigated the performance of some stochastic methods for solving constraint satisfaction (CSP) and fuzzy constraint satisfaction problems (FCSP). The purpose of this paper is to study the abilities of learning automaton in solving these problems and comparing it with other stochastic methods. The results confirm those of [2] and show its superiority to other methods.

Nowadays, path prediction is being extensively examined for use in the context of mobile and wireless computing towards more efficient network resource management schemes. Path prediction allows the network and services to further enhance the quality of service levels that the user enjoys. In this paper we present a path prediction algorithm that exploits the machine learning algorithm of learn...

Several mathematical distances between probabilistic languages have been investigated in the literature, motivated by applications in language modeling, computational biology, syntactic pattern matching and machine learning. In most cases, only pairs of probabilistic regular languages were considered. In this paper we extend previous results to pairs of languages generated by a probabilistic co...