Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition systems are in many cases trained from labeled "training" data (supervised learning), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning). This book delves into the following topics:
•Linear discriminant analysis
•Quadratic discriminant analysis
•Decision trees, decision lists
•Kernel estimation and K-nearest-neighbor algorithms
•Naive Bayes classifier
•Neural networks
•Support vector machines
.Boosting, Random Forest and Bagging
•Linear discriminant analysis
•Quadratic discriminant analysis
•Decision trees, decision lists
•Kernel estimation and K-nearest-neighbor algorithms
•Naive Bayes classifier
•Neural networks
•Support vector machines
.Boosting, Random Forest and Bagging
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