support-vector-machines
There are 1519 repositories under support-vector-machines topic.
Avik-Jain/100-Days-Of-ML-Code
100 Days of ML Coding
liuruoze/EasyPR
(CGCSTCD'2017) An easy, flexible, and accurate plate recognition project for Chinese licenses in unconstrained situations. CGCSTCD = China Graduate Contest on Smart-city Technology and Creative Design
accord-net/framework
Machine learning, computer vision, statistics and general scientific computing for .NET
kk7nc/Text_Classification
Text Classification Algorithms: A Survey
nsoojin/coursera-ml-py
Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera
mikeroyal/Machine-Learning-Guide
Machine learning Guide. Learn all about Machine Learning Tools, Libraries, Frameworks, Large Language Models (LLMs), and Training Models.
jmartinezheras/2018-MachineLearning-Lectures-ESA
Machine Learning Lectures at the European Space Agency (ESA) in 2018
fukuball/fuku-ml
Simple machine learning library / 簡單易用的機器學習套件
gionanide/Speech_Signal_Processing_and_Classification
Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].
snrazavi/Machine_Learning_2018
Codes and Project for Machine Learning Course, Fall 2018, University of Tabriz
DoubangoTelecom/compv
Insanely fast Open Source Computer Vision library for ARM and x86 devices (Up to #50 times faster than OpenCV)
Madhu009/Deep-math-machine-learning.ai
A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.
sharmapratik88/AIML-Projects
Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning
tiskw/random-fourier-features
Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model
Mamcose/NSL-KDD-Network-Intrusion-Detection
Machine Learning Algorithms on NSL-KDD dataset
MrKhan0747/Diabetes-Prediction
Machine learning approach to detect whether patien has the diabetes or not. Data cleaning, visualization, modeling and cross validation applied
GeorgeSeif/Python-Machine-Learning
Python Machine Learning Algorithms
hiroyuki-kasai/ClassifierToolbox
A MATLAB toolbox for classifier: Version 1.0.7
mithi/vehicle-tracking-2
A vehicle detection and tracking pipeline with OpenCV, histogram of oriented gradients (HOG), and support vector machines (SVM).
shawn-terryah/Twitter_Geolocation
Geolocating twitter users by the content of their tweets
harismuneer/Handwritten-Digits-Classification-Using-KNN-Multiclass_Perceptron-SVM
🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.
Neurojedi/Machine-Learning-Zero2Hero
A series of notebooks that introduce Machine Learning concepts with hands-on practice and its mathematics in brief.
CihanBosnali/Machine-Learning-without-Libraries
Today, using machine learning algorithms is as easy as "import knn from ..." but it doesn't really help if you want to learn how the algorithms work
hiroyuki-kasai/SparseGDLibrary
MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3
alicex2020/Deep-Learning-Lie-Detection
Use machine learning models to detect lies based solely on acoustic speech information
decile-team/jensen
A C++ toolkit for Convex Optimization (Logistic Loss, SVM, SVR, Least Squares etc.), Convex Optimization algorithms (LBFGS, TRON, SGD, AdsGrad, CG, Nesterov etc.) and Classifiers/Regressors (Logistic Regression, SVMs, Least Squares Regression etc.)
zhaoyichanghong/machine_learing_algo_python
implement the machine learning algorithms by python for studying
DSPKM/DSPKM
This is the page for the book Digital Signal Processing with Kernel Methods.
bank-of-england/Shapley_regressions
Statistical inference on machine learning or general non-parametric models
tirthajyoti/R-stats-machine-learning
Misc Statistics and Machine Learning codes in R
jrothschild33/Fudan-DataMining
2020 Spring Fudan University Data Mining Course HW by prof. Zhu Xuening. 复旦大学大数据学院2020年春季课程-数据挖掘（DATA620007）包含数据挖掘算法模型：Linear Regression Model、Logistic Regression Model、Linear Discriminant Analysis、K-Nearest Neighbour、Naive Bayes Classifier、Decision Tree Model、AdaBoost、Gradient Boosting Decision Tree(GBDT)、XGBoost、Random Forest Model、Support Vector Machine、Principal Component Analysis(PCA)
krish240574/ETFMachineLearning
Implementation of a paper in q/KDB+ and python - "Forecasting ETFs with Machine Learning Algorithms" - Jim Kyung-Soo Liew and Boris Mayster
pankajrawat9075/fantasy-sports-prediction
We have used our skill of machine learning along with our passion for cricket to predict the performance of players in the upcoming matches using ML Algorithms like random-forest and XG Boost
MaviccPRP/svm
Tutorial: Support Vector Machine from scratch using Python3
jaydipkumar/Data-Science-Python
Data Science Python Beginner Level Project
qandeelabbassi/python-svm-sgd
Python implementation of stochastic sub-gradient descent algorithm for SVM from scratch