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Although we are interested in learning networks of many interconnected units, let us begin by understanding how to learn the weights for a single perceptron Here the precise learning problem is to determine a weight vector that causes the perceptron to produce the correct f 1 output for each of the given training examples Several algorithms are known to solve this learning problem Here we consider two: the perceptron rule and the delta rule (a variant of the LMS rule used in 1 for learning evaluation functions) These two algorithms are guaranteed to converge to somewhat different acceptable hypotheses, under somewhat different conditions They are important to ANNs because they provide the basis for learning networks of many units One way to learn an acceptable weight vector is to begin with random weights, then iteratively apply the perceptron to each training example, modifying the perceptron weights whenever it misclassifies an example This process is repeated, iterating through the training examples as many times as needed until the perceptron classifies all training examples correctly Weights are modified at each step according to the perceptron training rule, which revises the weight wi associated with input xi according to the rule asp.net pdf 417 ASP . NET PDF-417 Barcode Generator - Generate 2D PDF417 in ...
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Easy-to-use ASP . NET PDF417 Barcode Component, generating PDF-417 barcode images in ASP.NET, C#, VB.NET, and IIS project. Here t is the target output for the current training example, o is the output generated by the perceptron, and q is a positive constant called the learning rate The role of the learning rate is to moderate the degree to which weights are changed at each step It is usually set to some small value (eg, 01) and is sometimes made to decay as the number of weight-tuning iterations increases Why should this update rule converge toward successful weight values To get an intuitive feel, consider some specific cases Suppose the training example is correctly classified already by the perceptron In this case, ( t - o) is zero, making Awi zero, so that no weights are updated Suppose the perceptron outputs a -1, when the target output is +1 To make the perceptron output a +1 instead of - 1 in this case, the weights must be altered to increase the value of G 2 For example, if xi r 0,then increasing wi will bring the perceptron closer to correctly classifying asp.net pdf 417 PDF417 ASP . NET - Barcode Tools
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It can work with Code128, PDF417 and many other symbologies. ... annoyingly split it along technology lines ( Barcode Professional "...for ASP . this example Notice the training rule will increase w, in this case, because (t - o), 7 , and Xi are all positive For example, if xi = 8, q = 01, t = 1 , and o = - 1 , then the weight update will be Awi = q(t - o)xi = O 1 ( 1 - (-1))08 = 016 On the other hand, if t = - 1 and o = 1, then weights associated with positive xi will be decreased rather than increased In fact, the above learning procedure can be proven to converge within a finite number of applications of the perceptron training rule to a weight vector that correctly classifies all training examples, provided the training examples are linearly separable and provided a sufficiently small 7 is used (see Minsky and Papert 1969) If the data are not linearly separable, convergence is not assured Although the perceptron rule finds a successful weight vector when the training examples are linearly separable, it can fail to converge if the examples are not linearly separable A second training rule, called the delta rule, is designed to overcome this difficulty If the training examples are not linearly separable, the delta rule converges toward a best-fit approximation to the target concept The key idea behind the delta rule is to use gradient descent to search the hypothesis space of possible weight vectors to find the weights that best fit the training examples This rule is important because gradient descent provides the basis for the BACKPROPAGATION algorithm, which can learn networks with many interconnected units It is also important because gradient descent can serve as the basis for learning algorithms that must search through hypothesis spaces containing many different types of continuously parameterized hypotheses The delta training rule is best understood by considering the task of training an unthresholdedperceptron; that is, a linear unit for which the output o is given by Thus, a linear unit corresponds to the first stage of a perceptron, without the threshold In order to derive a weight learning rule for linear units, let us begin by specifying a measure for the training error of a hypothesis (weight vector), relative to the training examples Although there are many ways to define this error, one common measure that will turn out to be especially convenient is asp.net pdf 417 Create PDF 417 barcode in asp . net WEB Application | DaniWeb
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