Topic: support-vector-machines Goto Github
Some thing interesting about support-vector-machines
Some thing interesting about support-vector-machines
support-vector-machines,Spam filtering module with Machine Learning using SVM (Support Vector Machines).
User: abdullahselek
Home Page: https://spampy.abdullahselek.com
support-vector-machines,Machine learning, computer vision, statistics and general scientific computing for .NET
Organization: accord-net
Home Page: http://accord-framework.net
support-vector-machines,Use machine learning models to detect lies based solely on acoustic speech information
User: alicex2020
support-vector-machines,Software designed to identify and monitor social/historical cues for short term stock movement
User: anfederico
support-vector-machines,# Machine Learning (Coursera) This is my solution to all the programming assignments and quizzes of Machine-Learning (Coursera) taught by Andrew Ng. After completing this course you will get a broad idea of Machine learning algorithms. Try to solve all the assignments by yourself first, but if you get stuck somewhere then feel free to browse the code. ## Contents * Lectures Slides * Solution to programming assignment * Solution to Quizzes by Andrew Ng, Stanford University, [Coursera](https://www.coursera.org/learn/machine-learning/home/welcome) ### Week 1 - [X] Videos: Introduction - [X] Quiz: Introduction - [X] Videos: Linear Regression with One Variable - [X] Quiz: Linear Regression with One Variable ### Week 2 - [X] Videos: Linear Regression with Multiple Variables - [X] Quiz: Linear Regression with Multiple Variables - [X] Videos: Octave/Matlab Tutorial - [X] Quiz: Octave/Matlab Tutorial - [X] Programming Assignment: Linear Regression ### Week 3 - [X] Videos: Logistic Regression - [X] Quiz: Logistic Regression - [X] Videos: Regularization - [X] Quiz: Regularization - [X] Programming Assignment: Logistic Regression ### Week 4 - [X] Videos: Neural Networks: Representation - [X] Quiz: Neural Networks: Representation - [X] Programming Assignment: Multi-class Classification and Neural Networks ### Week 5 - [X] Videos: Neural Networks: Learning - [X] Quiz: Neural Networks: Learning - [X] Programming Assignment: Neural Network Learning ### Week 6 - [X] Videos: Advice for Applying Machine Learning - [X] Quiz: Advice for Applying Machine Learning - [X] Videos: Programming Assignment: Regularized Linear Regression and Bias/Variance - [X] Machine Learning System Design - [X] Quiz: Machine Learning System Design ### Week 7 - [X] Videos: Support Vector Machines - [X] Quiz: Support Vector Machines - [X] Programming Assignment: Support Vector Machines ### Week 8 - [X] Videos: Unsupervised Learning - [X] Quiz: Unsupervised Learning - [X] Videos: Dimensionality Reduction - [X] Quiz: Principal Component Analysis - [X] Programming Assignment: K-Means Clustering and PCA ### Week 9 - [X] Videos: Anomaly Detection - [X] Quiz: Anomaly Detection - [X] Videos: Recommender Systems - [X] Quiz: Recommender Systems - [X] Programming Assignment: Anomaly Detection and Recommender Systems ### Week 10 - [X] Videos: Large Scale Machine Learning - [X] Quiz: Large Scale Machine Learning ### Week 11 - [X] Videos: Application Example: Photo OCR - [X] Quiz: Application: Photo OCR ## Certificate * [Verified Certificate]() ## References [[1] Machine Learning - Stanford University](https://www.coursera.org/learn/machine-learning)
User: ashleshk
support-vector-machines,100 Days of ML Coding
User: avik-jain
support-vector-machines,Statistical inference on machine learning or general non-parametric models
Organization: bank-of-england
support-vector-machines,ML algorithms from scratch
User: carmelgafa
support-vector-machines,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
User: cihanbosnali
support-vector-machines,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.)
Organization: decile-team
support-vector-machines,Insanely fast Open Source Computer Vision library for ARM and x86 devices (Up to #50 times faster than OpenCV)
User: doubangotelecom
Home Page: https://doubango.org
support-vector-machines,This is the page for the book Digital Signal Processing with Kernel Methods.
Organization: dspkm
support-vector-machines,Simple machine learning library / 簡單易用的機器學習套件
User: fukuball
Home Page: https://github.com/fukuball/FukuML-Tutorial
support-vector-machines,Python Machine Learning Algorithms
User: georgeseif
support-vector-machines,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].
User: gionanide
support-vector-machines,A MATLAB toolbox for classifier: Version 1.0.7
User: hiroyuki-kasai
support-vector-machines,MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3
User: hiroyuki-kasai
support-vector-machines,NTHU EE6550 Machine Learning slides and my code solutions for spring semester 2017.
User: howardyclo
support-vector-machines,Data Science Python Beginner Level Project
User: jaydipkumar
support-vector-machines,Machine Learning Lectures at the European Space Agency (ESA) in 2018
User: jmartinezheras
support-vector-machines,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)
User: jrothschild33
support-vector-machines,Text Classification Algorithms: A Survey
User: kk7nc
support-vector-machines,Implementation of a paper in q/KDB+ and python - "Forecasting ETFs with Machine Learning Algorithms" - Jim Kyung-Soo Liew and Boris Mayster
User: krish240574
support-vector-machines,(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
User: liuruoze
support-vector-machines,A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.
User: madhu009
Home Page: https://medium.com/deep-math-machine-learning-ai
support-vector-machines,Machine Learning Algorithms on NSL-KDD dataset
Organization: mamcose
support-vector-machines,Tutorial: Support Vector Machine from scratch using Python3
User: maviccprp
support-vector-machines,This is an exploration using synthetic data in CSV format to apply QML models for the sake of binary classification. You can find here three different approaches. Two with Qiskit (VQC and QK/SVC) and one with Pennylane (QVC).
User: maximer-v
support-vector-machines,Machine learning Guide. Learn all about Machine Learning Tools, Libraries, Frameworks, Large Language Models (LLMs), and Training Models.
User: mikeroyal
support-vector-machines,A vehicle detection and tracking pipeline with OpenCV, histogram of oriented gradients (HOG), and support vector machines (SVM).
User: mithi
Home Page: https://medium.com/@mithi/vehicles-tracking-with-hog-and-linear-svm-c9f27eaf521a
support-vector-machines,Machine learning approach to detect whether patien has the diabetes or not. Data cleaning, visualization, modeling and cross validation applied
User: mrkhan0747
support-vector-machines,Modelling Big Five Personality Inventory using Machine Learning algorithms
User: naveenkambham
support-vector-machines,detect objects using svm and opencv
User: neerajd12
support-vector-machines,The uploaded codes help to classify emails into spam and non spam classes by using Support Vector Machine classifier.
User: nishi1612
support-vector-machines,Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera
User: nsoojin
support-vector-machines,🏆 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.
Organization: osspk
Home Page: https://github.com/harismuneer
support-vector-machines,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
User: pankajrawat9075
support-vector-machines,The Fashion-MNIST dataset and machine learning models.
User: primaryobjects
support-vector-machines,Diabetes predictions application with gui
User: pritesh-ranjan
support-vector-machines,Python implementation of stochastic sub-gradient descent algorithm for SVM from scratch
User: qandeelabbassi
Home Page: https://medium.com/@qandeelabbassi/svm-implementation-from-scratch-python-2db2fc52e5c2
support-vector-machines,Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning
User: sharmapratik88
support-vector-machines,Geolocating twitter users by the content of their tweets
User: shawn-terryah
support-vector-machines,Codes and Project for Machine Learning Course, Fall 2018, University of Tabriz
User: snrazavi
support-vector-machines,A tiny and header-only C++ library aiming to be the fastest linear SVM solver.
Organization: softmin
support-vector-machines,Misc Statistics and Machine Learning codes in R
User: tirthajyoti
support-vector-machines,Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model
User: tiskw
Home Page: https://tiskw.github.io/random-fourier-features/
support-vector-machines,Here is my implementation of Support Vector Machine (SVM) & Transductive SVM (TSVM) using MATLAB.
User: vincent27hugh
support-vector-machines,Using various machine learning models to predict whether a company will go bankrupt
User: wangy8989
support-vector-machines,implement the machine learning algorithms by python for studying
User: zhaoyichanghong
support-vector-machines,Example of Machine Learning application: State of Charge estimation of a battery using SVR
User: zigarov
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