syuntai-lh,L,github

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Code for the paper "Language Models are Unsupervised Multitask Learners"

-l1-regularized-and-kernelized-optimization-problem-via-classic-qp-using-cvx

MATLAB implementation of the Support Vector Machine algorithm

-on-alk_release_code

Code for paper entitled "Optimal Neighborhood Multiple Kernel Clustering with Adaptive Local Kernels"

-robustacopf

Codes and data supplemental files for the paper "Robust Optimization for Electricity Generation"

-twin-support-vector-machines-for-pattern-classification

MATLAB Implementation of Twin Support Vector Machines

3ddwt-svm-gc

This is an implementation code of paper "Integration of 3-Dimensional Discrete Wavelet Transform and Markov Random Field for Hyperspectral Image Classification"

acc-2016

Code to generate figures from the paper "Semidefinite relaxations in optimal experiment design with application to substrate injection for hyperpolarized MRI" by John Maidens and Murat Arcak

advanced-project---ii

Analysis and Forecast of trendbreaks in aggregated aFRR demand, load time series

advanced_signal_processing_techniques

Familiarization with Higher Order Statistics (Spectra) and ARMA (Autoregressive Moving Average) models. Time Frequency Analysis techniques (Short Time Fourier, Hilbert-Huang and Wavelet Transform) are implemented in ECG signals.

bp_neural_networks_for_power_load_forecasting

使用BP神经网络进行电力系统短期负荷预测

bpforelectforecasting

KIISE 2014 conference-Title 'Electrical Load Forecasting using Back Propagation'

celebrity-face-recognition-webapp

This model classifies and recognizes sports personalities using OpenCV library and SVM algorithm after Wavelet transformation.

classification-in-eeg

Wavelet transform and SVM are used to identify awake or sleep based on EEG signal.

classification-methods-on-mnist-database

We implement three different types of classification methods on the MNIST dataset which consists of tens of thousands of hand-drawn images of digits from 0-9. The three methods we cover are Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Decision Tree classifiers. These methods delivered reasonable results in terms of their accuracy in classifying between the 10 digits, but these classification methods are outclassed by the more modern neural networks.

conic-optimization-for-quadratic-regression-under-sparse-noise

Suppport code for submission "Quadratic Regression Under Sparse Noise"

coursera_machine_learning

About this course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

crossproductkernel-noisydata

Experiments using a support vector machine with kernels on fuzzy sets on noise data

cudawnntimeseriesforecasting

A Local Linear Wavelet Neural Network trained by Particle Swarm Optimization implemented in CUDA for times series forecasting.

dasvm

Matlab implementation of the EM and MCMC algorithm for SVMs as introduced in the paper "Data augmentation for support vector machines"

dataset-load-forecasting-ampds

short term load forecasting using AmPDS dataset

dataset-stlfusingmlalgorithms

In this project( Short Term Load forecasting using ML algorithms) goal was to forecast approx. power consumption of a house based on the data set collected by ukdale. Thanks to Jack Kelly and William Knottenbelt for the data. Data link => http://jack-kelly.com/data/

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