machine learning code for Northeastern University CS6140 fall 2015

(1) Some models use Java multi-thread to optimize speed

- Regression Tree & Classification Tree

- Linear Regression

- Logistic Regression

- Perceptron

- Neural Network

- C-SVMs:
1) Linear kernel
2) Polynomial kernel
3) Gaussian kernel

- Gaussian Discriminant Analysis
1) Single Gaussian Model
2) Mixed Gaussian Model

- Naive Bayes:
1) Bernoulli + Multinoulli
2) Gaussian

- Boosting:
1) AdaBoost by classification tree for classification
2) GradientBoost by regression tree for regression and classification

- ECOC:
1) On top of AdaBoost
2) On top of SVMs

- Bagging / RandomForest on top of Tree

- KNN with various kernels

- PCA

(2) Implemented Gradient Decent framework to multi-thread update especially for neural network

(3) Designed and implemented Neural Network for any desired structure with NLL objective

(4) Implemented Cross-Validation process.

(5) Implemented two type of feature matrix:

- FullMatrix

- SparseMatrix