Tag: 機器學習技法

機器學習技法學習筆記 (7)：Radial Basis Function Network與Matrix Factorization

Posted on April 22, 2017 in AI.ML. View: 15,113

本篇內容涵蓋Radial Basis Function (RBF) Network、K-Means、One-Hot Encoding和Matrix Factorization

機器學習技法學習筆記 (6)：神經網路(Neural Network)與深度學習(Deep Learning)

Posted on April 17, 2017 in AI.ML. View: 35,249

本篇內容涵蓋神經網路(Neural Network, NN)、深度學習(Deep Learning, DL)、反向傳播算法(Backpropagation, BP)、Weight-elimination Regularizer、Early Stop、Autoencoder、Principal Component Analysis (PCA)

機器學習技法學習筆記 (5)：Boost Aggregation Models

Posted on April 02, 2017 in AI.ML. View: 5,011

本篇內容涵蓋AdaBoost (Adaptive Boost)、Gradient Boost、AdaBoosted Decision Tree和Gradient Boosted Decision Tree (GBDT)。

機器學習技法學習筆記 (4)：Basic Aggregation Models

Posted on March 29, 2017 in AI.ML. View: 8,368

本篇內容涵蓋Blending、Bagging、Decision Tree和Random Forest

機器學習技法學習筆記 (3)：Kernel Regression

Posted on March 15, 2017 in AI.ML. View: 6,291

本篇內容涵蓋Probabilistic SVM、Kernel Logistic Regression、Kernel Ridge Regression、Support Vector Regression (SVR)

機器學習技法學習筆記 (2)：Support Vector Machine (SVM)

Posted on February 20, 2017 in AI.ML. View: 11,884

本篇內容涵蓋Hard-Margin Support Vector Machine (SVM)、Kernel Function、Kernel Hard-Margin SVM、Soft-Margin SVM、Kernel Soft-Margin SVM、拉格朗日乘子法（Lagrange Multiplier）、Lagrangian Dual Problem

機器學習技法學習筆記 (1)：我們將會學到什麼? 先見林再來見樹

Posted on January 12, 2017 in AI.ML. View: 10,890

有什麼特徵可以使用？ / Embedding Numerous Features ：Kernel Models / Combining Predictive Features：Aggregation Models / Distilling Implicit Features：Extraction Models