By Mike Page (auth.), John A. Bullinaria BSc, MSc, PhD, David W. Glasspool BSc, Msc, George Houghton BA, MSc, PhD (eds.)
This quantity collects jointly refereed models of twenty-five papers provided on the 4th Neural Computation and Psychology Workshop, held at college collage London in April 1997. The "NCPW" workshop sequence is now good proven as a full of life discussion board which brings jointly researchers from such assorted disciplines as synthetic intelligence, arithmetic, cognitive technological know-how, desktop technology, neurobiology, philosophy and psychology to debate their paintings on connectionist modelling in psychology. the final topic of this fourth workshop within the sequence used to be "Connectionist Repre sentations", a subject matter which not just attracted individuals from most of these fields, yet from allover the realm in addition. From the viewpoint of the convention organisers targeting representational matters had the virtue that it instantly concerned researchers from all branches of neural computation. Being so vital either to psychology and to connectionist modelling, it's one region approximately which every person within the box has their very own powerful perspectives, and the variety and caliber of the displays and, simply as importantly, the dialogue which them, definitely attested to this.
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Extra info for 4th Neural Computation and Psychology Workshop, London, 9–11 April 1997: Connectionist Representations
The primitive nature of the operation paves the way for excellent performance. But it also imposes dire limitations. Only in certain types of task will we find the points mapping onto one output neatly separated by a line boundary from the points mapping onto the other. In other tasks the points will be dispersed in a more complex fashion. Thus the Percept ron method is only effective in certain cases. ' They pointed out that many tasks which we might consider to be straightforward, such as deciding on whether two binary values are the same - the so-called 'parity' task - do not fall into the linearly separable category.
If this is larger than the error goal, go to step Bl, otherwise, terminate the training session. 3 Methods For Training Two different algorithms for training the CSFN are proposed in this work. The first algorithm is based on updating the weights, the centres, and the opening angle in the same epoch as described above. e. Phase B) is executed but only weights are updated; centres and angles are not changed. Step C2 : One training epoch is executed where only the opening angles are adjusted; weights and centres remain same.
Step C3 : Another training epoch is performed to update the centres; weights and angles are not changed. 34 The Sum-Squared Error is computed after every training epoch. If the network output reaches the error goal, then training is stopped, otherwise, the algorithm is repeated from first step. 5 Lens Fitting Problem The database for fitting contact lenses [6J has been used as an application to test the training algorithm for conic section function network. The data set contains three classes where each class refers to the type of contact lenses with which the patient should be fitted.
4th Neural Computation and Psychology Workshop, London, 9–11 April 1997: Connectionist Representations by Mike Page (auth.), John A. Bullinaria BSc, MSc, PhD, David W. Glasspool BSc, Msc, George Houghton BA, MSc, PhD (eds.)