A novel modular connectionist architecture is presented in which the networks composing the architecture compete to learn the training patterns. As a result of the competition, different networks learn different training patterns and, thus, learn to compute different functions. The architecture performs task decomposition in the sense that it learns to partition a task into two or more functionally independent vii tasks and allocates distinct networks to learn each task. In addition, the architecture tends to allocate to each task the network whose topology is most appropriate to that task, and tends to allocate the same network to similar tasks and distinct networks to dissimilar tasks. Furthermore, it can be easily modified so as to...

An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Schapire, 1996; Schapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier – especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble’s performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees. 1.

Many real world problems quirea degree of flexibility that is to achieve using hand algorithms. One such domain is vision-based autonomous driving. In this task, the dual challenges of a constantly changing environment coupled with a real processing constrain the flexibility and of a machine learning system essential. This describes just such a learning system, called (Autonomous Land Vehicle In a Neural Network). It presents the neural network architecture and training techniques that allow to drive in a variety of including singlelane paved and unpaved roads, multilane lined and unlined roads, and obstacle-ridden on- and road environments, at speeds of up to 55 miles hour.

Deep dyslexia is an acquired reading disorder marked by the occurrence of semantic errors (e.g., reading RIVER as "ocean"). In addition, patients exhibit a number of other symptoms, including visual and morphological effects in their errors, a part-of-speech effect, and an advantage for concrete over abstract words. Deep dyslexia poses a distinct challenge for cognitive neuropsychology because there is little understanding of why such a variety of symptoms should co-occur in virtually all known patients. Hinton and Shallice (1991) replicated the co-occurrence of visual and semantic errors by lesioning a recurrent connectionist network trained to map from orthography to semantics. While the success of their simulations is encouraging, there is little understanding of what underlying principles are responsible for them. In this paper we evaluate and, where possible, improve on the most important design decisions made by Hinton and Shallice, relating to the task, the network architecture, the training procedure, and the testing procedure. We identify four properties of networks that underly their ability to reproduce the deep dyslexic symptom-complex: distributed orthographic and semantic representations, gradient descent learning, attractors for word meanings, and greater richness of concrete vs. abstract semantics. The first three of these are general connectionist principles and the last is based on earlier theorizing. Taken together, the results demonstrate the usefulness of a connectionist approach to understanding deep dyslexia in particular, and the viability of connectionist neuropsychology in general.

We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Our models represent objects as probabilistic constellations of rigid parts (features). The variability within a class is represented by a joint probability density function on the shape of the constellation and the appearance of the parts. Our method automatically identifies distinctive features in the training set. The set of model parameters is then learned using expectation maximization (see the companion paper [11] for details). When trained on different, unlabeled and unsegmented views of a class of objects, each component of the mixture model can adapt to represent a subset of the views. Similarly, different component models can also "specialize" on sub-classes of an object class. Experiments on images of human heads, leaves from different species of trees, and motor-cars demonstrate that the method works...

This paper reviews research on combining artificial neural nets, and provides an overview of, and an introduction to, the papers contained this Special Issue, and its companion (Connection Science, 9, 1). Two main approaches, ensemble-based, and modular, are identified and considered. An ensemble, or committee, is made up of a set of nets, each of which is a general function approximator. The members of the ensemble are combined in order to obtain better generalisation performance than would be achieved by any of the individual nets. The main issues considered here under the heading of ensemble-based approaches, are (a) how to combine the outputs of the ensemble members (b) how to create candidate ensemble members and (c) which methods lead to the most effective ensembles? Under the heading of modular approaches we begin by considering a divide-and-conquer approach by which a function is automatically decomposed into a number of subfunctions which are treated by specialis...

This paper discusses the linearly weighted combination of estimators in which the weighting functions are dependent on the input. We show that the weighting functions can be derived...

A neural-network ensemble is a very successful technique where the outputs of a set of separately trained neural network are combined to form one unified prediction. An effective ensemble should consist of a set of networks that are not only highly correct, but ones that make their errors on different parts of the input space as well; however, most existing techniques only indirectly address the problem of creating such a set. We present an algorithm called Addemup that uses genetic algorithms to explicitly search for a highly diverse set of accurate trained networks. Addemup works by first creating an initial population, then uses genetic operators to continually create new networks, keeping the set of networks that are highly accurate while disagreeing with each other as much as possible. Experiments on four real-world domains show that Addemup is able to generate a set of trained networks that is more accurate than several existing ensemble approaches. Experiments also show that Ad...

The method of temporal differences (TD) is a learning technique which specialises in predicting the likely outcome of a sequence over time. Examples of such sequences include speech frame vectors, whose outcome is a phoneme or word decision, and positions in a board game, whose outcome is a win/loss decision. Recent results by Tesauro in the domain of backgammon indicate that a neural network, trained by TD methods to evaluate positions generated by self-play, can reach an advanced level of backgammon skill. For my summer thesis project, I first implemented the TD/neural network learning algorithms and confirmed Tesauro's results, using the domains of tic-tac-toe and backgammon. Then, motivated by Waibel's success with modular neural networks for phoneme recognition, I experimented with using two modular architectures (DDD and Meta-Pi) in place of the monolithic networks. I found that using the modular networks significantly enhanced the ability of the backgammon evaluator to change it...

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