[Statistical Learning Methods Notes 04] Naive Bayes
Study notes on Statistical Learning Methods
Statistical Learning
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[Statistical Learning Methods Notes 03] k-Nearest Neighbors Method
The nearest neighbors algorithm assumes a training dataset is given in which the class of each instance is known. For classification, a new instance is predicted from the classes of its k nearest training instances by majority voting or similar rules. Therefore, the k-nearest neighbors method has no explicit learning process.
[Statistical Learning Methods Notes 02] Perceptron
The perceptron is a linear binary classification model. Its input is an instance's feature vector, and its output is the instance's class. Its goal is to find a separating hyperplane that linearly divides the training data.
[Statistical Learning Method Notes 01] Introduction to Statistical Learning
Statistical learning is the discipline in which computers build probabilistic and statistical models from data and use those models to predict and analyze data. These notes follow Li Hang's book *Statistical Learning Methods*.