By Alder M.
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Extra resources for An Introduction to Pattern Recognition
Again, there is a problem of how to measure the distances. In either alternative, it is possible to weight the count of points inversely by distance from the place of interest, so that remote points count less than close ones. This brings us back, yet again, to the question of what the right metric is. Some people have argued that Artificial Neural Nets are just a non-parametric statistical method of making decisions: this is debatable but not profitably. html [12/12/2000 4:04:18 AM] CART et al Next: Clustering: supervised v unsupervised Up: Decisions, decisions..
There are various forms of thresholder; the so called hard limiter just takes the sign of the output, if ax+by+c is positive, the unit outputs 1, if negative or zero it outputs -1. Some people prefer 0 to -1, but this makes no essential difference to the operation of the net. As described, the function applied to ax + by + c is called the sgn function, not to be confused with the sine function, although they sound the same. The network is, in some respects, easier to handle if the sigmoid function is smooth.
If a new data point is provided, we can calculate the height of the two hills at that point, and respectively. It is intuitively appealing to argue that if the male hill is higher than the female hill at the new point, then it is more likely that the new point is male than female. Indeed, we can say how much more likely by looking at the ratio of the two numbers, the so called likelihood ratio Moreover, we can fairly easily tell if a point is a long way from any data we have seen before because both the likelihoods and will be small.
An Introduction to Pattern Recognition by Alder M.