Artificial Mind System - Kernel Memory Approach by Tetsuya Hoya

By Tetsuya Hoya

This ebook is written from an engineer's point of view of the brain. "Artificial brain procedure" exposes the reader to a huge spectrum of fascinating parts usually mind technological know-how and mind-oriented stories. during this examine monograph an image of the holistic version of a man-made brain procedure and its behaviour is drawn, as concretely as attainable, inside of a unified context, which may ultimately bring about functional realisation by way of or software program. With a view that "the brain is a method constantly evolving", principles encouraged by means of many branches of stories concerning mind technological know-how are built-in in the textual content, i.e. man made intelligence, cognitive technology / psychology, connectionism, attention reports, normal neuroscience, linguistics, trend reputation / info clustering, robotics, and sign processing.

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Extra info for Artificial Mind System - Kernel Memory Approach

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Hereafter in this book, the terminology kernel 1 is then frequently referred to as (but not limited to) the kernel function K(a, b) which merely represents a certain distance metric between two vectors a and b. 1 depicts the kernel unit used in the kernel memory concept. As in the figure, a single kernel unit is composed of 1) the kernel function, 2) 1 In this book, the term kernel sometimes interchangeably represents “kernel unit”. 2 The Kernel Memory 33 excitation counter, 3) auxiliary memory to store the class ID (label), and 4) pointers to the other kernel units.

Nevertheless, it is stated that, by exploiting the flexible configuration property of a PNN, the separation of pattern space can be kept sufficiently well for each class even when adding new classes, as long as the amount of the training data is not excessive for each class. Then, as discussed above, this is supported by the empirical fact that the generalisation performance was not seriously deteriorated for almost all the cases. It can therefore be concluded that any “catastrophic” forgetting of the previously stored data due to accommodation of new classes did not occur, which meets Criterion 4).

This erosion was noticeable in the case of ISOLET. However, it can also be said that the degree of erosion is more or less bounded. In other words, the spread of the RBFs is limited, since, as shown in Figs. 8, the deterioration rate remained the same when the number of classes was increased. e. 0% for all the initial conditions. 4 Comparison Between Commonly Used Connectionist Models 25 16 (1) 14 Deterioration Rate (%) 12 Letter 1−2 (solid line (1)) Letter 1−4 Letter 1−8 Letter 1−16 (solid line (2)) 10 8 6 4 2 (2) 0 5 10 15 20 25 Number of New Classes Accommodated Fig.

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