The purpose of my doctorate work has consisted in the exploration of the potentialities and of the effectiveness of different neural classifiers, by experimenting their application in the solution of classification problems occurring in the fields of interest typical of the research group of the “Laboratorio Circuiti” at the Department of Electronic Engineering in Tor Vergata. Moreover, though inspired by works already developed by other scholars, the adopted neural classifiers have been partially modified, in order to add to them interesting peculiarities not present in the original versions, as well as to adapt them to the applications of interest. These applications can be grouped in two great families. As regards the first application, the objects to be classified are identified by features of static nature, while as regards the second family, the objects to be classified are identified by features evolving in time. In relation to the research fields taken as reference, the ones that belong to the first family are the following: • classification, by means of fuzzy algorithms, of acoustic signals, with the aim of attributing them to the source that generated them (recognition of musical instruments) • exclusive classification of simple human motor acts for the purpose of a precocious diagnosis of nervous system diseases The second family of application has been represented by that research field that aims to the development of neural tools for the Automatic Tanscription of piano pieces. The first part of this thesis has been devoted to the detailed description of the adopted neural classification techniques, as well as of the modifications introduced in order to improve their behavior in relation to the particular applications. In the second part, the experiments by means of which I have estimated the before-mentioned neural classification techniques have been introduced. It exactly deals with experiments carried out in the chosen research fields. For every application, the II results achieved have been reported; in some cases, the further steps to perform have also been proposed. After a brief introduction to the biological neural model, a description follows about the model of the artificial neuron that has afterwards inspired all the other models: the one proposed by McCulloch and Pitts in 1943. Subsequently, the different typologies of architectures that characterize neural networks are shortly introduced, as regards the feed-forward networks as well as the recursive networks. Then, a description of some learning strategies (supervised and unsupervised), adopted in order to train neural networks, is also given; some criteria by means of which one can estimate the goodness of an opportunely trained neural network are also given (errors made vs. generalization capability). A great part of the adopted networks is based on adaptations of the Backpropagation algorithm; the other networks have been instead trained by means of algorithms based on statistical or geometric criteria. The Backpropagation algorithm has been improved by augmenting the degrees of freedom to the learning ability of a feed-forward neural network with the introduction of a spline adaptive activation function. A wide description has been given of the recurrent neural networks and particularly of the locally recurrent neural networks, networks for dynamic classification exploited in the automatic transcription of piano music. After a more or less rigorous definition of the concepts of classification and clustering, some paragraphs have been devoted to some statistical and geometric neural architectures, exploited in the implementation of static classifiers of common use and in particular in the application fields that have regarded my doctorate work. A separate paragraph has been devoted to the Simpson’s classifier and to the variants originated from my research work. They have revealed themselves to be static classifiers very simple to implement and at the same time very ductile and efficient, in many situations as well as regards the problem of musical source recognition. Two have been the choices in this case. In the first one, III these classifiers have been trained, by means of a pure supervised learning approach, while in the second the training algorithm, though keeping a substantially supervised nature, is prepared by a clustering phase, with the aim of improving, in terms of errors and generalization, the covering of the input space. Subsequently, the locally recurrent neural networks seen as dynamic classifiers are retrieved. However, their training has been rethought according to the effective reduction of the classification error instead of the classic mean-square error. The last three paragraphs have been devoted to a detailed description, in terms of specifications, implementative choices and final results, of the aforesaid fields of applications. The results obtained in all the three fields of application can be considered encouraging. Particularly, the recognition of musical instruments by means of the adopted neural networks has shown results tha can be considered out comparable if not better than those obtained by means of other techniques, but with considerably less complex structures. In case of the Automatic Transcription of piano pieces, the dynamic networks I adopted have given good results. Unfortunately, the required computational resources required by such networks cannot be considered negligible. As far as the medical applications, we are still in an incipent phase of the research. However, opinions expressed by those people who work in this field can be considered substantially eulogistic. The research activities my doctorate work is part of have been carried out in collaboration with the Department “INFOCOM” of the first University of Rome “La Sapienza”, as far as the recognition of musical instruments and the Automatic Transcription of piano pieces. The necessity to study the potentialities of neural classifiers in medical application has instead come from a profitable existing collaboration with the Istituto Superiore di Sanità in Rome.
Carota, M. (2008). Neural network approach to problems of static/dynamic classification.
|Titolo:||Neural network approach to problems of static/dynamic classification|
|Data di pubblicazione:||26-ago-2008|
|Anno Accademico:||A.A. 2006/2007|
|Corso di dottorato:||Engineering of Sensory and Learning Systems|
|Settore Scientifico Disciplinare:||Settore ING-INF/01 - Elettronica|
|Tipologia:||Tesi di dottorato|
|Citazione:||Carota, M. (2008). Neural network approach to problems of static/dynamic classification.|
|Appare nelle tipologie:||07 - Tesi di dottorato|