. Instance-based learning algorithms are often faced with the problem of deciding which instances to store for use during generalization. Storing too many instances can result in large memory requirements and slow execution speed, and can cause an oversensitivity to noise. This paper has two main purposes. First, it provides a survey of existing algorithms used to reduce storage requirements in instance-based learning algorithms and other exemplar-based algorithms. Second, it proposes six additional reduction algorithms called DROP1--DROP5 and DEL (three of which were first described in Wilson & Martinez, 1997c, as RT1--RT3) that can be used to remove instances from the concept description. These algorithms and 10 algorithms from the survey are compared on 31 classification tasks. Of those algorithms that provide substantial storage reduction, the DROP algorithms have the highest average generalization accuracy in these experiments, especially in the presence of uniform class noise. ...

The paper describes an improved version of the Radial Basis Function algorithm, which integrates the advantages of Multi-Layer Perceptrons and Radial Basis Functions alone. The proposed paradigm is more general in nature, since it has the other two as particular subcases. It finds applications in several pattern recognition and classification tasks. Furthermore it can also be used as a method to map Fuzzy Inference Systems on Artificial Neural Networks.

Exemplar-based learning algorithms are often faced with the problem of deciding which instances or other exemplars to store for use during generalization. Storing too many exemplars can result in large memory requirements and slow execution speed, and can cause an oversensitivity to noise. This paper has two main purposes. First, it provides a survey of existing algorithms used to reduce the number of exemplars retained in exemplar-based learning models. Second, it proposes six new reduction algorithms called DROP1-5 and DEL that can be used to prune instances from the concept description. These algorithms and 10 algorithms from the survey are compared on 31 datasets. Of those algorithms that provide substantial storage reduction, the DROP algorithms have the highest generalization accuracy in these experiments, especially in the presence of noise.

There is a trend in recent OCR development to improve system performance by combining recognition results of several complementary algorithms. This thesis examines the classifier combination problem under strict separation of the classifier and combinator design. None other than the fact that every classifier has the same input and output specification is assumed about the training, design or implementation of the classifiers. A general theory of combination should possess the following properties. It must be able to combine anytype of classifiers regardless of the level of information contents in the outputs. In addition, a general combinator must be able to combine any mixture of classifier types and utilize all information available. Since classifier independence is difficult to achieve and to detect, it is essential for a combinator to handle correlated classifiers robustly. Although the performance of a robust (against correlation) combinator can be improved by adding classifiers indiscriminantly, it is generally of interest to achieve comparable performance with the minimum number of classifiers. Therefore, the combinator should have the ability to eliminate redundant classifiers. Furthermore, it is desirable to have a complexity control mechanism for the combinator. In the past, simplifications come from assumptions and constraints imposed by the system designers. In the general theory, there should be a mechanism to reduce solution complexity by exercising non-classifier-specific constraints. Finally, a combinator should capture classifier/image dependencies. Nearly all combination methods have ignored the fact that classifier performances (and outputs) depend on various image characteristics, and this dependency is manifested in classifier output patterns in relation to input imag...

If behavior networks, which use spreading activation to select actions, are analogous to connectionist methods of pattern recognition, then recurrent behavior networks, which use energy minimization, are analogous to Hopfield networks. Hopfield networks memorize patterns by making them attractors. Similarly, each behavior of a recurrent behavior network should be an attractor of the network, to inhibit fruitless, repeated switching between different behaviors in response to small changes in the environment and in motivations. I overcome two major objections to this view, and demonstrate that the performance in a test domain of the Do the Right Thing recurrent behavior network is improved by redesigning it to create desirable attractors and basins of attraction. I further show that this performance increase is correlated with an increase in persistence and a decrease in undesirable behavior-switching. On a more general level, this work encourages the study of action selection as a dynam...

In this paper, a hybrid learning approach named HDT is proposed. HDT simulates human reasoning by using symbolic leaming to do qualitative analysis and using neural leaming to do subsequent quantitative analysis. It generates the trunk of a binary hybrid decision tree according to the binary information gain ratio criterion in an instance space defined by only original unordered attributes. If unordered attributes cannot further distinguish training examples falling into a leaf node whose diversity is beyond the diversity-threshold, then the node is marked as a dummy node. After all those dummy nodes are marked, a specific feedforward neural network named Fnqc that is trained in an instance space defined by only original ordered attributes is exploited to accomplish the leaming task. Moreover, this paper distinguishes three kinds of incremental learning tasks. Two incremental leaming procedures designed for example-incremental leaming with different storage requirements are provided, which enables HDT to deal gracefully with data sets where new data are frequently appended. Also a hypothesis-driven constructive induction mechanism is provided, which enables HDT to generate compact concept descriptions.

1 In the last years fuzzy logic has gained the interest of a growing part of the scienti c community, due to some interesting properties which characterize it as against other kinds of Arti cial Intelligence

1 This paper presents a hybrid approach to the development of high-performance real-time intelligent and adaptive controllers for nonlinear plants. Several paradigms derived from cognitive sciences are considered and analyzed in this work, such as Neural Networks, Fuzzy Inference Systems, Genetic Algorithms, etc. Although most of these paradigms are widely known and have been used extensively in the eld of automatic control since several years, the novelty of the proposed approach resides in their tight integration and its capability of allowing a hybrid design. The di erent control strategies have also been integrated with the theory of Finite State Automata, in such a way that an automaton tracks the di erent plant states and selects accordingly one out of a given number of controller characteristics, each one being designed in a hybrid manner. State transitions can also be triggered by fuzzy and neural signals. Finally, two practical examples of the proposed hybrid approach are analyzed. 1

There are a great many books on Bayesian statistics available these days[]. This tutorial is being written since many of our papers and future work in gene arrays rely upon repeated use of these methods. Rather than continually have our readers refer to a variety of textbooks, we thought it best to have a

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