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Machine learning code, derivatives calculation and optimization algorithms developed during the Machine Learning course at Universidade de Sao Paulo. All codes in Python, NumPy and Matplotlib with example in the end of file.

classifier 최신버젼 다운로드

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# Classifier
[![DOI](https://zenodo.org/badge/279084447.svg)](https://zenodo.org/badge/latestdoi/279084447)

Python, NumPy and Matplotlib implementation from scratch of machine learning algorithms used for classification.

The training set with ***N*** elements is defined as ***D={(X1, y1), . . ., (XN, yN)}***, where ***X*** is a vector and ***y={0, 1}*** is one-hot encoded.

Sample code at the end of each file.

The **Neural_Network_Derivatives.pdf** document contains calculation of the derivatives used in code, except for the logistic regression that uses Yaoliang Yu lecture notes - see reference.

## Contents
**autoencoder:** Autoencoder with sigmoid activation function in 2nd and 4th layers.

**cnn:** 
Flexible architecture of Convolutional Neural Network, with sigmoid and relu activation functions. Setup the number of layers:
- Convolution layer.
- Pooling layer.
- Full connected layer.

Note: code translated from Matlab to Python. Original code in https://github.com/ClodoaldoLima/Convolutional-Neural-Networks---Matlab

**ensemble:** Implementation of three ensemble methods of neural networks:
1. Ensemble learing via negative correlation.
1. Ensemble Learning Using Decorrelated Neural Networks.
1. Creating Diversity In Ensembles Using Artiflcial Data (DECORATE).

**mixture of experts:** Two setups of mixture of experts for time series:
1. Linear models for experts and gating with softmax output.
1. Linear models for experts and gating with normalized gaussian output.

**neuralnets:** Single Layer Perceptron (SLP) and Multi Layer Perceptron (MLP).

**optimization:** first and second order methods used in machine learning backpropagation. Methods available:

1. Gradient Descent.
1. Bisection.
1. Newton.
1. Modified Newton.
1. Levenberg-Marquardt.
1. Quasi-Newton Methods.
1. One Step Secant.
1. Conjugate Gradient Methods.

**regression:** implementation of three setups of regression for classification:
1. Linear regression.
1. Linear regression with regularization.
1. Logistic regression.

**svm:** implementation of three models of Support Vector Machines for binary and multi-class classification.
1. Traditional Support Vector Machines (SVM).
1. Least Squares Support Vector Machines (LSSVM).
1. Twin Support Vector Machines (TWSVM).

Kernel types:
1. Linear
1. Polynomial
1. Radial Base Function (RBF)
1. Exponential Radial Base Function (ERBF)
1. Hyperbolic tangent (tanh)
1. Linear splines

### Acknowledgment
I would like to acknowledge professor Clodoaldo A. Moraes Lima for his guidance and support during the machine learning course at Universidade de Sao Paulo.

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