Damián López

Bio: Damián López is an academic researcher from Polytechnic University of Valencia. The author has contributed to research in topics: Grammar induction & Tree (data structure). The author has an hindex of 9, co-authored 41 publications receiving 224 citations.

Inference of reversible tree languages

Abstract: In this paper, we study the notion of k-reversibility and k-testability when regular tree languages are involved. We present an inference algorithm for learning a k-testable tree language that runs in polynomial time with respect to the size of the sample used. We also study the tree language classes in relation to other well known ones, and some properties of these languages are proven.

Error-correcting tree language inference

Abstract: A new tree language inference algorithm is proposed in this work. This algorithm extends a string language inference algorithm which is based on error correction (ECGI). The algorithm proposed here uses the substructures which have already been taken into account in a tree automaton, modifying the automaton in order to force it to accept the new structures presented in the identification process. The proposed algorithm allows the use of more powerful representation primitives in pattern recognition tasks than the string primitives. It also takes advantage of the thoroughly tested ECGI features used in speech and planar shape recognition tasks.

Error correcting analysis for tree languages

Abstract: To undertake a syntactic approach to a pattern recognition problem, it is necessary to have good grammatical models as well as good parsing algorithms that allow distorted samples to be classified. There are several methods that obtain, by taking two trees as input, the editing distance between them. In the following work, a polynomial time algorithm which processes the distance between a tree and a tree automaton is presented. This measure can be used in pattern recognition problems as an error model inside a syntactic classifier.

Syntactic Pattern Recognition by Error Correcting Analysis on Tree Automata

Abstract: Although the multidimensional primitives are more powerful than string primitives and there also exist some works concerning distance measure between multidimensional objects, there are no many applications of this kind of languages to syntactic pattern recognition tasks. In this work, multidimensional primitives are used for object modelling in a handwritten digit recognition task under a syntactic approach. Two well-known tree language inference algorithms are considered to build the models, using as error model an algorithm obtaining the editing distance between a tree automaton and a tree; the editing distance algorithm gives the measure needed to complete the classification. The experiments carried out show the good performance of the approach.

IgTM: an algorithm to predict transmembrane domains and topology in proteins.

Abstract: Due to their role of receptors or transporters, membrane proteins play a key role in many important biological functions. In our work we used Grammatical Inference (GI) to localize transmembrane segments. Our GI process is based specifically on the inference of Even Linear Languages. We obtained values close to 80% in both specificity and sensitivity. Six datasets have been used for the experiments, considering different encodings for the input sequences. An encoding that includes the topology changes in the sequence (from inside and outside the membrane to it and vice versa) allowed us to obtain the best results. This software is publicly available at: http://www.dsic.upv.es/users/tlcc/bio/bio.html We compared our results with other well-known methods, that obtain a slightly better precision. However, this work shows that it is possible to apply Grammatical Inference techniques in an effective way to bioinformatics problems.

Grammatical Inference: Learning Automata and Grammars

Abstract: The problem of inducing, learning or inferring grammars has been studied for decades, but only in recent years has grammatical inference emerged as an independent field with connections to many scientific disciplines, including bio-informatics, computational linguistics and pattern recognition. This book meets the need for a comprehensive and unified summary of the basic techniques and results, suitable for researchers working in these various areas. In Part I, the objects of use for grammatical inference are studied in detail: strings and their topology, automata and grammars, whether probabilistic or not. Part II carefully explores the main questions in the field: What does learning mean? How can we associate complexity theory with learning? In Part III the author describes a number of techniques and algorithms that allow us to learn from text, from an informant, or through interaction with the environment. These concern automata, grammars, rewriting systems, pattern languages or transducers.

Theory of átomata

Abstract: We show that every regular language defines a unique nondeterministic finite automaton (NFA), which we call "atomaton", whose states are the "atoms" of the language, that is, non-empty intersections of complemented or uncomplemented left quotients of the language. We describe methods of constructing the atomaton, and prove that it is isomorphic to the normal automaton of Sengoku, and to an automaton of Matz and Potthoff. We study "atomic" NFA's in which the right language of every state is a union of atoms. We generalize Brzozowski's double-reversal method for minimizing a deterministic finite automaton (DFA), showing that the result of applying the subset construction to an NFA is a minimal DFA if and only if the reverse of the NFA is atomic.

The application of machine vision to food and agriculture: a review

Abstract: This paper reviews developments since 2000 in the application of machine vision to food and agriculture. The subject involves applying radiation of various wavelengths to materials in order to find more about them: often this means looking not only at surfaces but also at internal structures. While visible light frequently provides enough useful information to make sound judgements, with the advent of dual energy X-ray (DEXA) detection, X-rays have been increasingly valuable. Perhaps the most exciting development is the 'spectral image cube' as an investigative tool. There have also been valuable developments in the use of three-dimensional methods, such as 'double Hough transforms' for the accurate delineation of crop rows, so that 'precision agriculture' can be realized, and the use of sets of visual calibration points so that robot vehicles can determine their exact locations and headings. Overall, the steady development of useful vision algorithms has been well matched by the capability of tod...

Syntactic pattern recognition

Abstract: These Ecole polytechnique federale de Lausanne EPFL, n° 305 (1978) Reference doi:10.5075/epfl-thesis-305Print copy in library catalog Record created on 2005-03-16, modified on 2016-08-08

STAMINA: a competition to encourage the development and assessment of software model inference techniques

Abstract: Models play a crucial role in the development and maintenance of software systems, but are often neglected during the development process due to the considerable manual effort required to produce them. In response to this problem, numerous techniques have been developed that seek to automate the model generation task with the aid of increasingly accurate algorithms from the domain of Machine Learning. From an empirical perspective, these are extremely challenging to compare; there are many factors that are difficult to control (e.g. the richness of the input and the complexity of subject systems), and numerous practical issues that are just as troublesome (e.g. tool availability). This paper describes the StaMinA (State Machine Inference Approaches) competiton, that was designed to address these problems. The competition attracted numerous submissions, many of which were improved or adapted versions of techniques that had not been subjected to extensive empirical evaluations, and had not been evaluated with respect to their ability to infer models of software systems. This paper shows how many of these techniques substantially improve on the state of the art, providing insights into some of the factors that could underpin the success of the best techniques. In a more general sense it demonstrates the potential for competitions to act as a useful basis for empirical software engineering by (a) spurring the development of new techniques and (b) facilitating their comparative evaluation to an extent that would usually be prohibitively challenging without the active participation of the developers.