By K. I. Diamantaras, S. Y. Kung
Systematically explores the connection among valuable part research (PCA) and neural networks. presents a synergistic exam of the mathematical, algorithmic, software and architectural points of crucial part neural networks. utilizing a unified formula, the authors current neural versions appearing PCA from the Hebbian studying rule and people which use least squares studying principles equivalent to back-propagation. Examines the rules of organic perceptual structures to give an explanation for how the mind works. each bankruptcy incorporates a chosen checklist of functions examples from assorted parts.
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Extra info for Principal Component Neural Networks: Theory and Applications
Example text
There are also hybrid-training methods. In a hybrid training method, you provide only some expected outputs. This training method is used with deep belief neural networks. Stochastic and Deterministic Training A deterministic training algorithm always performs exactly the same way, given the same initial state. There are typically no random numbers used in a deterministic training algorithm. Stochastic training makes use of random numbers. Because of this, an algorithm will always train differently, even with the same starting state.
3: Abstract Machine Learning Algorithm As you can see, the algorithm above accepts input and produces output. Most machine learning algorithms operate completely synchronously. The algorithm will only output when presented with input. It is not like a human brain, which always responds to input but occasionally produces output without input! So far we have only referred to the input and output patterns abstractly. You may be wondering exactly what they are. The input and output patterns are both vectors.
Many pattern recognition algorithms are something like a hash table in traditional programming. In traditional programming, a hash table is used to map keys to values. In many ways, a hash table is somewhat like a dictionary, in that it includes a term and its meaning. A hash table could look like the following: “hear” -> “to perceive or apprehend by the ear”“run” -> “to go faster than a walk”“write” -> “to form (as characters or symbols) on a surface with an instrument (as a pen)”The above example is a mapping between words and their definitions.