5 research outputs found
Recommended from our members
A study of instance-based algorithms for supervised learning tasks : mathematical, empirical, and psychological evaluations
This dissertation introduces a framework for specifying instance-based algorithms that can solve supervised learning tasks. These algorithms input a sequence of instances and yield a partial concept description, which is represented by a set of stored instances and associated information. This description can be used to predict values for subsequently presented instances. The thesis of this framework is that extensional concept descriptions and lazy generalization strategies can support efficient supervised learning behavior.The instance-based learning framework consists of three components. The pre-processor component transforms an instance into a more palatable form for the performance component, which computes the instance's similarity with a set of stored instances and yields a prediction for its target value(s). Therefore, the similarity and prediction functions impose generalizations on the stored instances to inductively derive predictions. The learning component assesses the accuracy of these prediction(s) and updates partial concept descriptions to improve their predictive accuracy.This framework is evaluated in four ways. First, its generality is evaluated by mathematically determining the classes of symbolic concepts and numeric functions that can be closely approximated by IB_1, a simple algorithm specified by this framework. Second, this framework is empirically evaluated for its ability to specify algorithms that improve IB_1's learning efficiency. Significant efficiency improvements are obtained by instance-based algorithms that reduce storage requirements, tolerate noisy data, and learn domain-specific similarity functions respectively. Alternative component definitions for these algorithms are empirically analyzed in a set of five high-level parameter studies. Third, this framework is evaluated for its ability to specify psychologically plausible process models for categorization tasks. Results from subject experiments indicate a positive correlation between a models' ability to utilize attribute correlation information and its ability to explain psychological phenomena. Finally, this framework is evaluated for its ability to explain and relate a dozen prominent instance-based learning systems. The survey shows that this framework requires only slight modifications to fit these highly diverse systems. Relationships with edited nearest neighbor algorithms, case-based reasoners, and artificial neural networks are also described
Selección diferenciada del conjunto de entrenamiento en redes de neuronas mediante aprendizaje retardado
Las Redes de Neuronas de Base Radial (RNBR) son aproximadores universales, en el sentido de que son capaces de aproximar, con el grado de precisión deseado, cualquier función continua multivariable, siempre que dispongan de un número suficiente de unidades ocultas. Estas redes se caracterizan por poseer características locales, ya que sus neuronas utilizan funciones de activación cuyo valor decrece exponencialmente al alejarse el patrón de entrada de sus centros. Las RNBR son modelos robustos frente a los errores en los datos y su entrenamiento es muy rápido en comparación con otros tipos de redes de neuronas. El principal inconveniente de las RNBR reside en su deficiente capacidad de generalización. Esto se debe a que es necesario un gran número de neuronas ocultas para poder construir una aproximación a la función objetivo mediante la suma de aproximaciones locales, especialmente si la dimensión del espacio de entrada es alta; este elevado número de neuronas ocultas puede influir negativamente en la capacidad de generalización. Se ha comprobado que el nivel de generalización de las redes de neuronas depende significativamente de la calidad de los datos de entrenamiento, y algunos de esos datos pueden ser redundantes o irrelevantes. Con una cuidadosa selección de los patrones de entrenamiento se podría mejorar la capacidad de generalización. Por otra parte, los métodos de aprendizaje retardado o “perezoso” pueden tener una buena capacidad de generalización pues construyen las representaciones de la función objetivo de forma local dependiendo de la nueva muestra de test, pero su precisión en la generalización depende significativa mente del número de patrones que se seleccionen y de la función de distancia utilizada. El objetivo principal de esta tesis consiste en mejorar la capacidad de generalización de las RNBR utilizando un enfoque basado en los métodos de aprendizaje retardado. Para ello, se propone un método de aprendizaje que selecciona automáticamente, del conjunto de entrenamiento, los patrones más apropiados para aproximar cada nueva muestra de test. Este método sigue una estrategia de aprendizaje perezoso, en el sentido de que construye aproximaciones locales centradas alrededor de la nueva muestra. También se pretende que este método sea general, aplicable independientemente del modelo de red de neuronas elegido; de este modo, se podrá aplicar a otros tipos de redes, como el perceptron multicapa. Para evaluar el modelo propuesto, se aplica a diferentes dominios que son representativos de problemas de aproximación de funciones, de predicción de series temporales y de clasificación. Los resultados obtenidos se comparan con los de los métodos de entrenamiento tradicionales, donde se entrenan las RNBR con todas las muestras de entrenamiento disponibles. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Radial Basis Neural Networks (RBNN) are universal approxirnators, in
the sense that they are able to approxirnate arbitrarily well any continuous
multivariate function, if enough hidden units are provided. These networks
have local characteristics, since their neurons use activation functions whose
value exponentially decreases when the input pattern moves away from its
centers. RNBR are robust modeis and its convergence is very fast compared
to other neural models.
A poor generalization ability is the main drawback of RNBR. A great
number of hidden neurons is necessary to build an approxirnation to the
objective function by rneans of the sum of local approxirnations, specially if
the dirnension of the input space is high; this high number of hidden neurons
can influence negatively to the network performance. It has been shown that
the level of generalization of neural networks depends on the quality of the
training data, and sorne of those data can be redundant or irrelevant. With a
careful selection of the training patterns, better generalization performance
rnay be obtained.
Qn the other hand, lazy learning rnethods can obtain a good generaliza
tion performance because they construct local representations of the objec
tive function depending on the new test sample, but its accuracy depends
significantly on the number of selected patterns and on the distance function
used.
The rnain goal of this thesis consists of improving the generalization
ability of RNBR using a lazy learning approach. Thus, a learning rnethod
that autornatically selects relevant data to answer a particular novel pattern
is proposed. This rnethod follows a lazy learning strategy, in the sense that
it builds local approxirnations centered around the new sample.
Another goal is that the method is applicable independently of the neural
rnodel chosen; in this way, it will be possible to apply the rnethod to other
types of networks, as rnultilayer perceptron.
In order to evaluate the proposed model, it is applied to different domains
that are representative of approxirnation function problerns, tirne series pre
diction problerns, and classification problerns. Results are cornpared with
those of the traditional RBNN training methods, where all the exarnples
frorn the training set are used to train the networks
Information Filters And Their Implementation In The Syllog System
this paper the notion of information filters. Information in a learning system flows from the experiences that the system is facing, through the acquistion procedure to the knowledge base, and thence to the problem solver. An information filter is any process that removes information at any stage of this flow. We call filters that are inserted between the experience space and the acquisition procedure data filters, and filters that are inserted between the acquisition procedure and the problem solver knowledge filters. INFORMATION FILTERIN