SVM | BestHub

Tencent Cloud Developer

Jul 4, 2024 · Artificial Intelligence

Football Match Outcome Prediction and Betting Strategy Using Machine Learning

The study combines team statistics and bookmaker odds with machine‑learning models—including Poisson, regression, Bayesian, SVM, Random Forest, DNN, and LSTM—to predict football match outcomes, identify confidence‑based betting intervals that yield profit, and suggests extensions to broader data, features, and financial trading.

SVMdata miningfootball prediction

0 likes · 23 min read

Football Match Outcome Prediction and Betting Strategy Using Machine Learning

Python Programming Learning Circle

Apr 10, 2024 · Artificial Intelligence

Top 10 Machine Learning Algorithms Explained

This article introduces the No‑Free‑Lunch principle in machine learning and provides concise explanations of ten fundamental algorithms—including linear and logistic regression, LDA, decision trees, Naïve Bayes, K‑Nearest Neighbors, LVQ, SVM, bagging with random forests, and boosting with AdaBoost—guiding beginners on how to choose the right model.

AIAlgorithmsSVM

0 likes · 14 min read

Top 10 Machine Learning Algorithms Explained

DaTaobao Tech

Mar 4, 2024 · Artificial Intelligence

Iris Classification with Machine Learning: Data Exploration and Classic Algorithms

This beginner-friendly guide walks through loading the classic Iris dataset, performing exploratory data analysis, and implementing four fundamental classifiers—Decision Tree, Logistic Regression, Support Vector Machine, and K‑Nearest Neighbors—complete with training, visualization, and accuracy evaluation, illustrating a full machine‑learning workflow.

SVMclassificationdecision tree

0 likes · 22 min read

Iris Classification with Machine Learning: Data Exploration and Classic Algorithms

Model Perspective

Jan 7, 2023 · Artificial Intelligence

Mastering Supervised Learning: From Linear Models to SVMs and Beyond

An extensive overview of supervised learning introduces key concepts, model types, loss functions, optimization methods, linear and generalized linear models, support vector machines, generative approaches, tree and ensemble techniques, as well as foundational learning theory, providing a comprehensive foundation for AI practitioners.

AIGenerative ModelsSVM

0 likes · 9 min read

Mastering Supervised Learning: From Linear Models to SVMs and Beyond

Tencent Cloud Developer

Dec 2, 2022 · Artificial Intelligence

Football Match Prediction Using Machine Learning and Betting Strategy Analysis

The study applies machine‑learning models—including logistic regression, SVM, random forest, deep neural networks and a DNN‑SVM ensemble—to 17‑dimensional team features and 51‑dimensional bookmaker odds, achieving up to 54.5% match‑outcome accuracy, proposing a profit‑condition betting strategy and extending the approach to stock‑price forecasting.

Betting StrategySVMSports Analytics

0 likes · 21 min read

Football Match Prediction Using Machine Learning and Betting Strategy Analysis

Model Perspective

Aug 8, 2022 · Artificial Intelligence

Mastering sklearn.svm: Parameters, Grid Search, and Real-World Examples

An in‑depth guide to sklearn.svm explains SVM classification and regression, details key parameters such as C and kernel types, demonstrates how to use GridSearchCV for hyperparameter tuning, and provides complete Python code examples for iris classification and California housing price prediction.

GridSearchCVPythonSVM

0 likes · 6 min read

Mastering sklearn.svm: Parameters, Grid Search, and Real-World Examples

Model Perspective

Aug 6, 2022 · Artificial Intelligence

How Kernel Functions Enable SVMs to Classify Non‑Linear Data

When training data from two classes overlap heavily, linear SVMs fail, so we map inputs into a high‑dimensional Hilbert (feature) space using kernel functions—such as linear, polynomial, radial basis, and Fourier kernels—to make the data linearly separable, formulate a quadratic programming problem, solve its convex dual, and construct a classifier for unknown samples.

Hilbert spaceSVMkernel methods

0 likes · 2 min read

How Kernel Functions Enable SVMs to Classify Non‑Linear Data

Model Perspective

Aug 4, 2022 · Artificial Intelligence

How Support Vector Machines Classify Data: Core Principles Explained

Support Vector Machines (SVM), introduced in 1992, are powerful data‑mining methods based on statistical learning theory that excel at handling small‑sample, nonlinear, and high‑dimensional regression and classification tasks, with distinct formulations for classification (SVC) and regression (SVR).

SVMclassificationmachine learning

0 likes · 5 min read

How Support Vector Machines Classify Data: Core Principles Explained

UC Tech Team

Nov 5, 2018 · Artificial Intelligence

News Page Identification Using Machine Learning: Feature Engineering, Model Selection, and Evaluation

To accurately distinguish news pages from other web page types, this study formulates the task as a binary classification problem, extracts 19 engineered features from HTML, evaluates logistic regression and SVM models with cross‑validation, and achieves over 90% precision, recall, and F1‑score using LR with Newton method.

SVMbinary classificationfeature engineering

0 likes · 13 min read

News Page Identification Using Machine Learning: Feature Engineering, Model Selection, and Evaluation

Tencent Cloud Developer

Oct 18, 2018 · Artificial Intelligence

10 Machine Learning Algorithms You Should Know to Become a Data Scientist

This article outlines the essential role of a data scientist and introduces ten fundamental machine‑learning algorithms—including PCA/SVD, OLS and polynomial regression, regularized linear models, K‑Means, logistic regression, SVM, feed‑forward, convolutional and recurrent neural networks, CRFs, ensemble trees, and reinforcement‑learning methods—while linking to popular Python libraries and tutorials.

AlgorithmsNeural NetworksPCA

0 likes · 10 min read

10 Machine Learning Algorithms You Should Know to Become a Data Scientist

iQIYI Technical Product Team

Dec 15, 2017 · Artificial Intelligence

Sentiment Classification of iQIYI User Comments: Model Selection, Feature Engineering, and Online Deployment

The team built a lightweight three‑class sentiment classifier for iQIYI user comments using a linear‑kernel SVM with high‑dimensional bag‑of‑words features and an expanded ~100k word lexicon, achieving over 96% accuracy across domains, and deployed it as a Spring Boot PMML service with zero‑downtime refresh, while planning GBDT‑enhanced features and word‑embedding optimizations.

NLPSVMText Classification

0 likes · 13 min read

Sentiment Classification of iQIYI User Comments: Model Selection, Feature Engineering, and Online Deployment

Qunar Tech Salon

Aug 22, 2017 · Artificial Intelligence

Sliding Window and SVM Based Web Crawler Detection System Design

This article describes a flexible web crawler identification approach that combines sliding‑window data collection with Support Vector Machine classification, detailing the underlying concepts, feature extraction, system architecture, client‑server interaction, and deployment steps for practical use.

Client-ServerSVMSliding Window

0 likes · 7 min read

Sliding Window and SVM Based Web Crawler Detection System Design

360 Zhihui Cloud Developer

Jun 16, 2017 · Artificial Intelligence

How We Built an Automated Video Quality Monitoring System with Machine Learning

This article details the design and implementation of a live‑stream video quality monitoring platform that combines subjective labeling, machine‑learning models, and a multi‑layer architecture to automatically evaluate and alert on video quality across thousands of streams.

SVMdeep learninglive streaming

0 likes · 10 min read

How We Built an Automated Video Quality Monitoring System with Machine Learning

Architects Research Society

Nov 17, 2016 · Artificial Intelligence

Comparative Study of Machine Learning Classifiers and Guidance for Algorithm Selection

The article summarizes a JMLR 2014 study that evaluated 179 classifiers across 121 UCI datasets, finding Random Forests and Gaussian‑kernel SVMs to be top performers, provides a review of supervised learning algorithms, and includes visual guidance for selecting appropriate machine‑learning methods.

SVMalgorithm selectionclassifier comparison

0 likes · 3 min read

Comparative Study of Machine Learning Classifiers and Guidance for Algorithm Selection

Architects Research Society

Oct 28, 2016 · Artificial Intelligence

Phishing Website Detection Using Machine Learning Models in R

This article presents a step‑by‑step machine‑learning analysis of the UCI Phishing Websites dataset in R, loading the data, training boosted logistic regression, SVM, tree‑bagging, and random‑forest models, comparing their accuracies, and identifying the most important predictive features for phishing detection.

RSVMcaret

0 likes · 11 min read

Phishing Website Detection Using Machine Learning Models in R

Qunar Tech Salon

Aug 18, 2016 · Artificial Intelligence

Automatic Ticket Classification Using SVM and word2vec at Qunar

At Qunar, the data center algorithm team developed an automatic ticket classification system that combines Support Vector Machine with word2vec embeddings to handle high‑dimensional, low‑sample text data, achieving 89% accuracy and 80% recall while outlining the full machine‑learning pipeline from feature extraction to deployment.

QunarSVMText Classification

0 likes · 7 min read

Automatic Ticket Classification Using SVM and word2vec at Qunar

Qunar Tech Salon

Mar 28, 2015 · Artificial Intelligence

Support Vector Machines in R: Theory, Implementation, and Parameter Tuning

This article explains how support vector machines work, how to handle non‑linear and multi‑class problems, and provides a complete R implementation using the e1071 package, including linear and radial kernels, model evaluation, parameter tuning, and visualisation with grid plots.

Grid PlotRRadial Kernel

0 likes · 9 min read

Support Vector Machines in R: Theory, Implementation, and Parameter Tuning

Qunar Tech Salon

Mar 15, 2015 · Artificial Intelligence

Overview of Common Classification Algorithms in Data Mining

This article introduces the concepts of classification and prediction in data mining, outlines their workflow, and provides concise explanations of six widely used classification techniques—decision trees, K‑Nearest Neighbour, Support Vector Machine, Vector Space Model, Bayesian methods, and neural networks—highlighting their principles, advantages, and limitations.

SVMbayesianclassification

0 likes · 9 min read

Overview of Common Classification Algorithms in Data Mining