Machine Learning Mastery

NOVEMBER 21, 2023

The Support Vector Machine algorithm is one of the most popular supervised machine learning techniques, and it comes implemented in the OpenCV library. This tutorial will introduce the necessary skills to start using Support Vector Machines in OpenCV, using a custom dataset that we will generate.

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KDnuggets

SEPTEMBER 12, 2019

This article explains the Support Vector Machines (SVM) algorithm in an easy way.

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Analytics Vidhya

JUNE 3, 2022

Introduction Classification problems are often solved using supervised learning algorithms such as Random Forest Classifier, Support Vector Machine, Logistic Regressor (for binary class classification) etc. The post One Class Classification Using Support Vector Machines appeared first on Analytics Vidhya.

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Support Vector Machines Supervised Learning Data Science Algorithm 297

Analytics Vidhya

OCTOBER 6, 2021

Ever wondered, how great would it be, if we could predict, whether our request for a loan, will be approved or not, simply by the use of machine learning, from the ease and comfort […]. The post Loan Status Prediction using Support Vector Machine (SVM) Algorithm appeared first on Analytics Vidhya.

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Analytics Vidhya

OCTOBER 23, 2020

The post The Mathematics Behind Support Vector Machine Algorithm (SVM) appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon. Introduction One of the classifiers that we come across while learning about.

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Support Vector Machines Algorithm Data Science Analytics 298

Analytics Vidhya

JULY 9, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction A Support Vector Machine (SVM) is a very powerful and. The post Support Vector Machine and Principal Component Analysis Tutorial for Beginners appeared first on Analytics Vidhya.

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Analytics Vidhya

MARCH 8, 2021

ArticleVideo Book Objective Learn how the support vector machine works Understand the role and types of kernel functions used in an SVM. The post Beginner’s Guide to Support Vector Machine(SVM) appeared first on Analytics Vidhya. Introduction.

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Support Vector Machines Analytics Analytics Machine Learning 250

KDnuggets

AUGUST 23, 2022

This post focuses on building an intuition of the Support Vector Machine algorithm in a classification context and an in-depth understanding of how that graphical intuition can be mathematically represented in the form of a loss function. We will also discuss kernel tricks and a more useful variant of SVM with a soft margin.

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Dataconomy

MARCH 12, 2025

Support Vector Machines (SVM) are a cornerstone of machine learning, providing powerful techniques for classifying and predicting outcomes in complex datasets. What are Support Vector Machines (SVM)? They work by identifying a hyperplane that best separates distinct classes within the data.

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Analytics Vidhya

JULY 5, 2021

The post Start Learning SVM (Support Vector Machine) Algorithm Here! ArticleVideo Book This article was published as a part of the Data Science Blogathon Source Overview In this article, we will learn the working of. appeared first on Analytics Vidhya.

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Support Vector Machines Algorithm Data Science Analytics 271

KDnuggets

JULY 10, 2023

A guide to understanding support vector machines for classification: from theory to scikit-learn implementation.

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Analytics Vidhya

APRIL 26, 2021

The post Introduction to SVM(Support Vector Machine) Along with Python Code appeared first on Analytics Vidhya. ArticleVideo Book This article was published as a part of the Data Science Blogathon. Introduction This article aims to provide a basic understanding.

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Support Vector Machines Python Data Science Analytics 271

Flipboard

NOVEMBER 28, 2023

In a previous tutorial, we have explored the use of the Support Vector Machine algorithm as one of the most popular supervised machine learning techniques that comes implemented in the OpenCV library.

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Analytics Vidhya

DECEMBER 23, 2024

Hinge loss is pivotal in classification tasks and widely used in Support Vector Machines (SVMs), quantifies errors by penalizing predictions near or across decision boundaries. By promoting robust margins between classes, it enhances model generalization.

Machine Learning 205

Machine Learning Machine Learning Support Vector Machines Analytics 205

Analytics Vidhya

MARCH 26, 2020

Unlocking a New World with the Support Vector Regression Algorithm Support Vector Machines (SVM) are popularly and widely used for classification problems in machine. The post Support Vector Regression Tutorial for Machine Learning appeared first on Analytics Vidhya.

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Machine Learning Machine Learning Support Vector Machines Algorithm 358

Analytics Vidhya

OCTOBER 16, 2022

Introduction Support vector machine is one of the most famous and decorated machine learning algorithms in classification problems. This article was published as a part of the Data Science Blogathon.

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Support Vector Machines Algorithm Machine Learning Machine Learning 394

Hacker News

SEPTEMBER 2, 2023

Since its inception in "Attention Is All You Need", transformer architecture has led to revolutionary advancements in NLP.

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Analytics Vidhya

MARCH 28, 2024

Introduction The One-Class Support Vector Machine (SVM) is a variant of the traditional SVM. It is specifically tailored to detect anomalies. Its primary aim is to locate instances that notably deviate from the standard.

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KDnuggets

AUGUST 7, 2019

It is impossible to learn all their mechanics; however, many algorithms sprout from the most established algorithms, e.g. ordinary least squares, gradient boosting, support vector machines, tree-based algorithms and neural networks.

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Analytics Vidhya

MAY 20, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Before the sudden rise of neural networks, Support Vector Machines. The post Top 15 Questions to Test your Data Science Skills on SVM appeared first on Analytics Vidhya.

Data Science 229

Data Science Support Vector Machines Analytics Analytics 229

Mlearning.ai

MAY 12, 2023

Support Vector Machine: A Comprehensive Guide — Part1 Support Vector Machines (SVMs) are a type of supervised learning algorithm used for classification and regression analysis. Submission Suggestions Support Vector Machine: A Comprehensive Guide — Part1 was originally published in MLearning.ai

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SAS Software

FEBRUARY 11, 2025

Fitting a Support Vector Machine (SVM) Model - Learn how to fit a support vector machine model and use your model to score new data In Part 6, Part 7, Part 9, Part 10, and Part 11 of this series, we fit a logistic regression, decision tree, random forest, gradient [.]

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Data Science Dojo

MAY 24, 2023

Support vector machines : Support vector machines are a more complex algorithm that can be used for both classification and regression tasks. They work by dividing the data into smaller and smaller groups until each group can be classified with a high degree of accuracy.

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Machine Learning Machine Learning Data Scientist Support Vector Machines 284

Mlearning.ai

MAY 22, 2023

Support Vector Machine: A Comprehensive Guide — Part2 In my last article, we discussed SVMs, the geometric intuition behind SVMs, and also Soft and Hard margins. Transformed Data into 2-D Data Conclusion Support Vector Machines (SVMs) offer a powerful framework for classification and regression tasks.

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Data Science Dojo

FEBRUARY 7, 2023

They are also used in machine learning, such as support vector machines and k-means clustering. Robust inference: Robust inference is a technique that is used to make inferences that are not sensitive to outliers or extreme observations.

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Data Science Dojo

FEBRUARY 15, 2023

Zheng’s “Guide to Data Structures and Algorithms” Parts 1 and Part 2 1) Big O Notation 2) Search 3) Sort 3)–i)–Quicksort 3)–ii–Mergesort 4) Stack 5) Queue 6) Array 7) Hash Table 8) Graph 9) Tree (e.g.,

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Dataconomy

MARCH 6, 2025

Common types of surrogate models Surrogate modeling encompasses various machine learning methodologies, including: Polynomial regressions: Useful for capturing relationships in a straightforward manner. Support vector machines: Effective in high-dimensional spaces and can handle nonlinearities.

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Data Science Dojo

FEBRUARY 14, 2024

However, it can be very effective when you are working with multivariate analysis and similar methods, such as Principal Component Analysis (PCA), Support Vector Machine (SVM), K-means, Gradient Descent, Artificial Neural Networks (ANN), and K-nearest neighbors (KNN).

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Data Science Dojo

MAY 27, 2024

Some common models used are as follows: Logistic Regression – it classifies by predicting the probability of a data point belonging to a class instead of a continuous value Decision Trees – uses a tree structure to make predictions by following a series of branching decisions Support Vector Machines (SVMs) – create a clear decision (..)

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Machine Learning (Theory)

MARCH 5, 2025

Support Vector Machines were disrupted by deep learning, and convolutional neural networks were displaced by transformers. As an example, the speech recognition community spent decades focusing on Hidden Markov Models at the expense of other architectures, before eventually being disrupted by advancements in deep learning.

AI 157

AI AI Support Vector Machines Deep Learning 157

Flipboard

JANUARY 24, 2025

The proposed Q-BGWO-SQSVM approach utilizes an improved quantum-inspired binary Grey Wolf Optimizer and combines it with SqueezeNet and Support Vector Machines to exhibit sophisticated performance. SqueezeNet’s fire modules and complex bypass mechanisms extract distinct features from mammography images.

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Data Science Dojo

MAY 1, 2023

Scikit-learn Scikit-learn is a powerful library for machine learning in Python. It provides a wide range of tools for supervised and unsupervised learning, including linear regression, k-means clustering, and support vector machines. Scikit-learn is a go-to tool for data scientists and machine learning practitioners.

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Python Machine Learning Machine Learning Data Science 221

Towards AI

FEBRUARY 23, 2024

Clustering in Machine Learning stands as a fundamental unsupervised learning task, different from its supervised counterparts due to the lack of labeled data. As… Read the full blog for free on Medium.

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Clustering Support Vector Machines Machine Learning Machine Learning 115

Data Science Dojo

MAY 1, 2023

Scikit-learn Scikit-learn is a powerful library for machine learning in Python. It provides a wide range of tools for supervised and unsupervised learning, including linear regression, k-means clustering, and support vector machines. Scikit-learn is a go-to tool for data scientists and machine learning practitioners.

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Python Machine Learning Machine Learning Data Science 195

Pickl AI

JANUARY 21, 2025

Support Vector Machines (SVM) SVMs are powerful classification algorithms that work by finding the hyperplane that best separates different classes in high-dimensional space. Commonly used algorithms include Support Vector Machines, Random Forests, and Gradient Boosting methods; however, selection should be based on testing.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Towards AI

APRIL 5, 2023

Photo by David Schultz on Unsplash Linfa Linfa is a Rust-based machine-learning library that offers a wide range of algorithms for regression, classification, clustering, and other tasks. One of Linfa’s most notable features is its emphasis on interoperability, achieved through a standardized API for machine learning algorithms.

Machine Learning 98

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Flipboard

NOVEMBER 10, 2024

Subsequently, based on the aforementioned multimodal indices, a support vector machine was employed to investigate the machine learning (ML) classification of PD patients with normal cognition (PDNC) and PDMCI. The performance of 29 classifiers was assessed based on various combinations of indicators.

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Machine Learning Machine Learning Support Vector Machines ML 73