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Advances in Artificial Intelligence: 4th Helenic Conference on AI, SETN 2006, Heraklion, Crete, Greece, May 18-20, 2006, Proceedings

Grigoris Antoniou ; George Potamias ; Costas Spyropoulos ; Dimitris Plexousakis (eds.)

En conferencia: 4º Hellenic Conference on Artificial Intelligence (SETN) . Heraklion, Crete, Greece . May 18, 2006 - May 20, 2006

Resumen/Descripción – provisto por la editorial

No disponible.

Palabras clave – provistas por la editorial

Artificial Intelligence (incl. Robotics); Information Systems Applications (incl. Internet); Information Storage and Retrieval; Algorithm Analysis and Problem Complexity; Image Processing and Computer Vision; Database Management

Disponibilidad
Institución detectada Año de publicación Navegá Descargá Solicitá
No detectada 2006 SpringerLink

Información

Tipo de recurso:

libros

ISBN impreso

978-3-540-34117-8

ISBN electrónico

978-3-540-34118-5

Editor responsable

Springer Nature

País de edición

Reino Unido

Fecha de publicación

Información sobre derechos de publicación

© Springer-Verlag Berlin Heidelberg 2006

Tabla de contenidos

An Improved Hybrid Genetic Clustering Algorithm

Yongguo Liu; Jun Peng; Kefei Chen; Yi Zhang

In this paper, a new genetic clustering algorithm called IHGA-clustering is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGA-clustering, DHB operation is developed to improve the individual and accelerate the convergence speed, and partition-mergence mutation operation is designed to reassign objects among different clusters. Equipped with these two components, IHGA-clustering can stably output the proper result. Its superiority over HGA-clustering, GKA, and KGA-clustering is extensively demonstrated for experimental data sets.

- Full Papers | Pp. 192-202

A Greek Named-Entity Recognizer That Uses Support Vector Machines and Active Learning

Georgios Lucarelli; Ion Androutsopoulos

We present a named-entity recognizer for Greek person names and temporal expressions. For temporal expressions, it relies on semi- automatically produced patterns. For person names, it employs two Support Vector Machines, that scan the input text in two passes, and active learning, which reduces the human annotation effort during training.

- Full Papers | Pp. 203-213

Intelligent Segmentation and Classification of Pigmented Skin Lesions in Dermatological Images

Ilias Maglogiannis; Elias Zafiropoulos; Christos Kyranoudis

During the last years, computer vision-based diagnostic systems have been used in several hospitals and dermatology clinics, aiming mostly at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer, versus other types of non-malignant cutaneous diseases. In this paper we discuss intelligent techniques for the segmentation and classification of pigmented skin lesions in such dermatological images. A local thresholding algorithm is proposed for skin lesion separation and border, texture and color based features, are then extracted from the digital images. Extracted features are used to construct a classification module based on Support Vector Machines (SVM) for the recognition of malignant melanoma versus dysplastic nevus.

- Full Papers | Pp. 214-223

Modelling Robotic Cognitive Mechanisms by Hierarchical Cooperative CoEvolution

Michail Maniadakis; Panos Trahanias

The current work addresses the development of cognitive abilities in artificial organisms. In the proposed approach, neural network-based agent structures are employed to represent distinct brain areas. We introduce a Hierarchical Cooperative CoEvolutionary (HCCE) approach to design autonomous, yet collaborating agents. Thus, partial brain models consisting of many substructures can be designed. Replication of lesion studies is used as a means to increase reliability of brain model, highlighting the distinct roles of agents. The proposed approach effectively designs cooperating agents by considering the desired pre- and post- lesion performance of the model. In order to verify and assess the implemented model, the latter is embedded in a robotic platform to facilitate its behavioral capabilities.

- Full Papers | Pp. 224-234

Bayesian Feature Construction

Manolis Maragoudakis; Nikos Fakotakis

The present paper discusses the issue of enhancing classification performance by means other than improving the ability of certain Machine Learning algorithms to construct a precise classification model. On the contrary, we approach this significant problem from the scope of an extended coding of training data. More specifically, our method attempts to generate more features in order to reveal the hidden aspects of the domain, modeled by the available training examples. We propose a novel feature construction algorithm, based on the ability of Bayesian networks to represent the conditional independence assumptions of a set of features, thus projecting relational attributes which are not always obvious to a classifier when presented in their original format. The augmented set of features results in a significant increase in terms of classification performance, a fact that is depicted to a plethora of machine learning domains (i.e. data sets from the UCI ML repository and the Artificial Intelligence group) using a variety of classifiers, based on different theoretical backgrounds.

- Full Papers | Pp. 235-245

Musical Instrument Recognition and Classification Using Time Encoded Signal Processing and Fast Artificial Neural Networks

Giorgos Mazarakis; Panagiotis Tzevelekos; Georgios Kouroupetroglou

Traditionally, musical instrument recognition is mainly based on frequency domain analysis (sinusoidal analysis, cepstral coefficients) and shape analysis to extract a set of various features. Instruments are usually classified using k-NN classifiers, HMM, Kohonen SOM and Neural Networks. In this work, we describe a system for the recognition of musical instruments from isolated notes. We are introducing the use of a Time Encoded Signal Processing method to produce simple matrices from complex sound waveforms, for instrument note encoding and recognition. These matrices are presented to a Fast Artificial Neural Network (FANN) to perform instrument recognition with promising results in organ classification and reduced computational cost. The evaluation material consists of 470 tones from 19 musical instruments synthesized with 5 wide used synthesizers (Microsoft Synth, Creative SB Live! Synth, Yamaha VL-70m Tone Generator, Edirol Soft-Synth, Kontakt Player) and 84 isolated notes from 20 western orchestral instruments (Iowa University Database).

- Full Papers | Pp. 246-255

O-DEVICE: An Object-Oriented Knowledge Base System for OWL Ontologies

Georgios Meditskos; Nick Bassiliades

This paper reports on the implementation of a rule system, called O-DEVICE, for reasoning about OWL instances using deductive rules. O-DEVICE exploits the rule language of the CLIPS production rule system and transforms OWL ontologies into an object-oriented schema of COOL. During the transformation procedure, OWL classes are mapped to COOL classes, OWL properties to class slots and OWL instances to COOL objects. The purpose of this transformation is twofold: a) to exploit the advantages of the object-oriented representation and access all the properties of instances in one step, since properties are encapsulated inside resource objects; b) to be able to use a deductive object-oriented rule language for querying and creating maintainable views of OWL instances, which operates over the object-oriented schema of CLIPS, and c) to answer queries faster, since the implied relationships due to the rich OWL semantics have been pre-computed. The deductive rules are compiled into CLIPS production rules. The rich open-world semantics of OWL are partly handled by the incremental transformation procedure and partly by the rule compilation procedure.

- Full Papers | Pp. 256-266

Abduction for Extending Incomplete Information Sources

Carlo Meghini; Yannis Tzitzikas; Nicolas Spyratos

The extraction of information from a source containing term-classified objects is plagued with uncertainty, due, among other things, to the possible incompleteness of the source index. To overcome this incompleteness, the study proposes to expand the index of the source, in a way that is as reasonable as possible with respect to the original classification of objects. By equating reasonableness with logical implication, the sought expansion turns out to be an explanation of the index, captured by abduction. We study the general problem of query evaluation on the extended information source, providing a polynomial time algorithm which tackles the general case, in which no hypothesis is made on the structure of the taxonomy. We then specialize the algorithm for two well-know structures: DAGs and trees, showing that each specialization results in a more efficient query evaluation.

- Full Papers | Pp. 267-278

Post Supervised Based Learning of Feature Weight Values

Vassilis S. Moustakis

The article presents in detail a model for the assessment of feature weight values in context of inductive machine learning. Weight assessment is done based on learned knowledge and can not be used to assess feature values prior to learning. The model is based on Ackoff’s theory of behavioral communication. The model is also used to assess rule value importance. We present model heuristics and present a simple application based on the “play” vs. “not play” golf application. Implications about decision making modeling are discussed.

- Full Papers | Pp. 279-289

Recognition of Greek Phonemes Using Support Vector Machines

Iosif Mporas; Todor Ganchev; Panagiotis Zervas; Nikos Fakotakis

In the present work we study the applicability of Support Vector Machines (SVMs) on the phoneme recognition task. Specifically, the Least Squares version of the algorithm (LS-SVM) is employed in recognition of the Greek phonemes in the framework of telephone-driven voice-enabled information service. The N-best candidate phonemes are identified and consequently feed to the speech and language recognition components. In a comparative evaluation of various classification methods, the SVM-based phoneme recognizer demonstrated a superior performance. Recognition rate of 74.2% was achieved from the N-best list, for N=5, prior to applying the language model.

- Full Papers | Pp. 290-300