Roadmap Motivation of Aggregation Uniform Blending Linear and Any Blending Bagging (Bootstrap Aggregation) Summary…

从这一节开始学习机器学习技法课程中的SVM, 这一节主要介绍标准形式的SVM: Linear SVM 引入SVM 首先回顾Percentron Learning Algrithm(感知器算法PLA)是如何分类的,如下图,找到一条线,将两类训练数据点分开即可: PLA的最后的直线可能有很多条,那到底哪条好呢?好坏的标准则是其泛化性能,即在测试数据集上的正确率,如下,下面三条直线都能正确的分开训练数据,那到底哪个好呢?SVM就是解决这个问题的. SVM求解 直觉告诉我们最右的要好一些,因为测试数据的…

Roadmap Motivation of Aggregation Uniform Blending Linear and Any Blending Bagging (Bootstrap Aggregation) Summary…

原文地址:https://www.jianshu.com/p/7ff6fd6fc99f 问题描述 程序实现 13-15 # coding:utf-8 # decision_tree.py import numpy as np def ReadData(dataFile): with open(dataFile, 'r') as f: lines = f.readlines() data_list = [] for line in lines: line = line.strip().split(…

Roadmap Motivation of Boosting Diversity by Re-weighting Adaptive Boosting Algorithm Adaptive Boosting in Action Summary…

Roadmap Linear Network Hypothesis Basic Matrix Factorization Stochastic Gradient Descent Summary of Extraction Models Summary…

Roadmap Feature Exploitation Techniques Error Optimization Techniques Overfitting Elimination Techniques Machine Learning in Practice Summary…

Roadmap Motivation Neural Network Hypothesis Neural Network Learning Optimization and Regularization Summary…

Roadmap RBF Network Hypothesis RBF Network Learning k-Means Algorithm k-Means and RBF Network in Action Summary…

Roadmap Decision Tree Hypothesis Decision Tree Algorithm Decision Tree Heuristics in C&RT Decision Tree in Action Summary…