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Additional resources for Adaptive, Learning and Pattern Recognition Systems: Theory and Applications
These are the so-called adaptive procedures. T h e most commonly made 28 R. 0. 45) g w = (Wi x ) ci + 9 where w iis the ith weight vector, (wi , x) is the inner product of w iand x, and ci is a constant for the ith class (Nilsson, 1965). A vector x is classified by forming these rn linear functions, and by assigning x to the category corresponding to the largest discriminant function. 48) c assigning x to w1 if g ( x ) > 0 and to augmented vectors a and y by a = x) w2 if g ( x ) I:[ < 0. 50) we can write g(x) in the homogeneous form ‘The problem of designing such a classifier is the problem of finding an (augmented) weight vector a from a set of sample patterns.
Or consider the problem of obtaining extremely large amounts of hand-printed data to design a classifier. If each character must be identified correctly, very careful and time-consuming human monitoring and checking will be necessary. If the classifier can continually improve its performance on unidentified data coming from typical documents, much painstaking work can be avoided. T h e problem of learning from unidentified samples (sometimes called unsupervised learning, or learning without a teacher) presents both theoretical and practical problems.
X,; di 1 Ftn) If the classifier decides to take an additional measurement, then the measurement must be optimally selected from the remaining features F, in order to minimize the risk. 40) Again, Eq. 40) can be recursively solved by setting the terminal condition to be and computing backwards for risk functions R , , n < N. T h e major 50 K. S. FU difference between the solution of Eq. 40) and that of Eq.
Adaptive, Learning and Pattern Recognition Systems: Theory and Applications by Mendel