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A collection of 1000 handwritten 0s and 1000 handwritten 1s are collected. This data is divided into 667 for training and 333 for test in each class; the two classes are 0 and 1. Each pattern is a matrix of 16 rows and 12 columns. We divide each pattern, both training and test, into four parts. Each part consists of four rows (4 × 12 = 48 pixels). The first part consists of rows 1–4; the second part has rows 5–8; rows 9–12 are in the third part; and the fourth part has rows 13–16. 9. 6 Summary 1.

4. : Pattern Recognition and Machine Learning. : Pattern Classification and Scene Analysis. : Introduction to Statistical Pattern Recognition. : Empirical performance analysis of linear discriminant classifiers, In: Proceedings of Computer Vision and Pattern Recognition, 25–28 June 1998, pp. 164–169. Santa Barbara, CA, USA (1998) Chapter 3 Perceptron Abstract Perceptron is a well-known classifier based on a linear discriminant function. It is intrinsically a binary classifier. It has been studied extensively in its early years and it provides an excellent platform to appreciate classification based on Support Vector Machines.

3 Classification based on vectors y X a 29 x1 x2 x1 ∨ x2 0 0 1 1 0 1 0 1 0 1 1 1 Pattern number Class label 1 x1 x2 1 2 3 4 –1 1 1 1 0 0 1 1 0 1 0 1 –1 1 1 1 We view it as a two-class problem where the output 0 is taken as indicating the negative class and output 1 is an indicator of the positive class. 3. We start with W0 = (0, 0, 0)t . The stepwise updates on W are: 1. W0 misclassifies the first vector (−1, 0, 0)t as the dot product between them is 0. So, W1 = W0 + (−1, 0, 0)t = (−1, 0, 0)t .

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