Pattern+classification

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Unformatted text preview: Contents 1 Introduction 3 1.1 Machine Perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 An Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.1 Related fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3 The Sub-problems of Pattern Classification . . . . . . . . . . . . . . . 11 1.3.1 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3.2 Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.3.3 Overfitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.3.4 Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.3.5 Prior Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.3.6 Missing Features . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.3.7 Mereology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.3.8 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.3.9 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.3.10 Invariances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.3.11 Evidence Pooling . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.3.12 Costs and Risks . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.3.13 Computational Complexity . . . . . . . . . . . . . . . . . . . . 16 1.4 Learning and Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.4.1 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . 16 1.4.2 Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . 17 1.4.3 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . 17 1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Summary by Chapters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Bibliographical and Historical Remarks . . . . . . . . . . . . . . . . . . . . 19 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1 2 CONTENTS Chapter 1 Introduction T he ease with which we recognize a face understand spoken words read handwrit-ten characters identify our car keys in our pocket by feel and decide whether an apple is ripe by its smell belies the astoundingly complex processes that underlie these acts of pattern recognition. Pattern recognition — the act of taking in raw data and taking an action based on the “category” of the pattern — has been crucial for our survival and over the past tens of millions of years we have evolved highly sophisticated neural and cognitive systems for such tasks. 1.1 Machine Perception It is natural that we should seek to design and build machines that can recognize patterns. From automated speech recognition fingerprint identification optical char-acter recognition DNA sequence identification and much more it is clear that reli-able accurate pattern recognition by machine would be immensely useful. Moreover able accurate pattern recognition by machine would be immensely useful....
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