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Python Programming Learning Circle
Python Programming Learning Circle
Sep 11, 2025 · Artificial Intelligence

Essential Machine Learning Algorithms: From Linear Regression to DBSCAN

This article provides a comprehensive overview of key machine‑learning algorithms—including supervised methods like linear regression, SVM, Naive Bayes, logistic regression, k‑NN, decision trees, random forests, GBDT, and unsupervised techniques such as k‑means, hierarchical clustering, DBSCAN, and PCA—explaining their principles, strengths, and typical use cases.

AlgorithmsNaive BayesUnsupervised Learning
0 likes · 20 min read
Essential Machine Learning Algorithms: From Linear Regression to DBSCAN
AI Code to Success
AI Code to Success
Mar 28, 2025 · Artificial Intelligence

Unlocking the Power of Support Vector Machines: Theory, Code, and Real‑World Uses

This comprehensive guide explores Support Vector Machines—from their historical roots and core mathematical principles to practical Python implementations, visualization techniques, and diverse applications such as image recognition, text classification, bioinformatics, and financial risk assessment—while also weighing their strengths and limitations.

PythonSupport Vector Machineclassification
0 likes · 19 min read
Unlocking the Power of Support Vector Machines: Theory, Code, and Real‑World Uses
Tencent Cloud Developer
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.

Random Forestdata miningfootball prediction
0 likes · 23 min read
Football Match Outcome Prediction and Betting Strategy Using Machine Learning
Python Programming Learning Circle
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.

AIRandom Forestlinear regression
0 likes · 14 min read
Top 10 Machine Learning Algorithms Explained
DaTaobao Tech
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.

classificationdecision treeiris dataset
0 likes · 22 min read
Iris Classification with Machine Learning: Data Exploration and Classic Algorithms
Model Perspective
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 Modelslinear models
0 likes · 9 min read
Mastering Supervised Learning: From Linear Models to SVMs and Beyond
Tencent Cloud Developer
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 StrategyData ScienceRandom Forest
0 likes · 21 min read
Football Match Prediction Using Machine Learning and Betting Strategy Analysis
Model Perspective
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.

GridSearchCVPythonmachine learning
0 likes · 6 min read
Mastering sklearn.svm: Parameters, Grid Search, and Real-World Examples
Model Perspective
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 spacekernel methodsmachine learning
0 likes · 2 min read
How Kernel Functions Enable SVMs to Classify Non‑Linear Data
Model Perspective
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).

classificationsvm
0 likes · 5 min read
How Support Vector Machines Classify Data: Core Principles Explained
Baobao Algorithm Notes
Baobao Algorithm Notes
Jan 5, 2022 · Artificial Intelligence

When to Use Logistic Regression, SVM, Decision Trees, and More? A Practical Frequency Guide

This article analyzes how often common machine‑learning algorithms such as k‑NN, Naïve Bayes, decision trees, SVM, logistic regression, and neural networks are used in industry, explains their typical scenarios, highlights strengths and weaknesses, and shows how non‑linearity and feature engineering affect their suitability.

algorithm comparisondecision treefeature engineering
0 likes · 12 min read
When to Use Logistic Regression, SVM, Decision Trees, and More? A Practical Frequency Guide
Programmer DD
Programmer DD
Nov 21, 2018 · Artificial Intelligence

What I Learned From My AI Engineer Interview: Recommendation Systems, TF‑IDF, Word2Vec & SVM Explained

A Java developer shares his self‑learning journey into AI, recounts a technical interview covering recommendation system types, TF‑IDF similarity metrics, word2vec behavior modeling, and SVM fundamentals, and reflects on the challenges and resources that helped him transition into algorithm engineering.

AIRecommendation Systemsinterview
0 likes · 7 min read
What I Learned From My AI Engineer Interview: Recommendation Systems, TF‑IDF, Word2Vec & SVM Explained
UC Tech Team
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.

Web Crawlingbinary classificationfeature engineering
0 likes · 13 min read
News Page Identification Using Machine Learning: Feature Engineering, Model Selection, and Evaluation
Tencent Cloud Developer
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.

AlgorithmsDecision TreesNeural Networks
0 likes · 10 min read
10 Machine Learning Algorithms You Should Know to Become a Data Scientist
Hulu Beijing
Hulu Beijing
Jan 9, 2018 · Artificial Intelligence

Mastering SVM: How Kernel Functions and Slack Variables Enable Perfect Classification

This article explains how kernel functions and slack variables empower Support Vector Machines to achieve zero training error on linearly inseparable data, presents three theoretical questions about Gaussian kernels, error‑free classification without slack variables, and the impact of the regularization parameter C when using SMO, and provides detailed analytical solutions.

SMOkernel functionsslack variables
0 likes · 6 min read
Mastering SVM: How Kernel Functions and Slack Variables Enable Perfect Classification
iQIYI Technical Product Team
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.

DeploymentNLPSentiment Analysis
0 likes · 13 min read
Sentiment Classification of iQIYI User Comments: Model Selection, Feature Engineering, and Online Deployment
Hulu Beijing
Hulu Beijing
Nov 14, 2017 · Artificial Intelligence

Are Projected Points Still Linearly Separable? SVM Insight & Proof

This article examines whether points from two linearly separable classes remain separable after being projected onto the SVM decision hyperplane, providing geometric and convex‑optimization proofs along with illustrative diagrams and references for deeper study.

convex optimizationhyperplane projectionlinear separability
0 likes · 7 min read
Are Projected Points Still Linearly Separable? SVM Insight & Proof
Qunar Tech Salon
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.

Sliding WindowSystem Architectureclient-server
0 likes · 7 min read
Sliding Window and SVM Based Web Crawler Detection System Design
MaGe Linux Operations
MaGe Linux Operations
May 7, 2017 · Artificial Intelligence

Big Data & Machine Learning: Core Definitions and Essential Algorithms

This article explains what big data and machine learning are, their interrelationship, various big‑data analysis approaches, core machine‑learning concepts, and details ten fundamental algorithms—including regression, neural networks, SVM, clustering, dimensionality reduction, and recommendation—while highlighting their roles in modern data‑driven applications.

Big DataNeural Networksclustering
0 likes · 24 min read
Big Data & Machine Learning: Core Definitions and Essential Algorithms
Architects Research Society
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.

Random Forestalgorithm selectionclassifier comparison
0 likes · 3 min read
Comparative Study of Machine Learning Classifiers and Guidance for Algorithm Selection
Architects Research Society
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.

RRandom Forestcaret
0 likes · 11 min read
Phishing Website Detection Using Machine Learning Models in R
Qunar Tech Salon
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.

QunarWord2Vecmachine learning
0 likes · 7 min read
Automatic Ticket Classification Using SVM and word2vec at Qunar
Qunar Tech Salon
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 PlotParameter TuningR
0 likes · 9 min read
Support Vector Machines in R: Theory, Implementation, and Parameter Tuning
Qunar Tech Salon
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.

Bayesiandata miningdecision tree
0 likes · 9 min read
Overview of Common Classification Algorithms in Data Mining