# Tag: Support Vector Machines (26)

**How can quantum computing be useful for Machine Learning**- Apr 12, 2019.

We investigate where quantum computing and machine learning could intersect, providing plenty of use cases, examples and technical analysis.**Supervised Learning: Model Popularity from Past to Present**- Dec 28, 2018.

An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?**Journey to Machine Learning – 100 Days of ML Code**- Sep 7, 2018.

A personal account from Machine Learning enthusiast Avik Jain on his experiences of #100DaysOfMLCode, a challenge that encourages beginners to code and study machine learning for at least an hour, every day for 100 days.**Support Vector Machine (SVM) Tutorial: Learning SVMs From Examples**- Aug 28, 2017.

In this post, we will try to gain a high-level understanding of how SVMs work. I’ll focus on developing intuition rather than rigor. What that essentially means is we will skip as much of the math as possible and develop a strong intuition of the working principle.**KDnuggets™ News 17:n31, Aug 16: Data Science Primer: Basic Concepts; Python vs R vs rest**- Aug 16, 2017.

Also: What Artificial Intelligence and Machine Learning Can Do-And What It Can't; How Convolutional Neural Networks Accomplish Image Recognition?; Making Predictive Models Robust: Holdout vs Cross-Validation; The Machine Learning Abstracts: Support Vector Machines**The Machine Learning Abstracts: Support Vector Machines**- Aug 10, 2017.

While earlier entrants in this series covered elementary classification algorithms, another (more advanced) machine learning algorithm which can be used for classification is Support Vector Machines (SVM).**How to squeeze the most from your training data**- Jul 27, 2017.

In many cases, getting enough well-labelled training data is a huge hurdle for developing accurate prediction systems. Here is an innovative approach which uses SVM to get the most from training data.**Building Regression Models in R using Support Vector Regression**- Mar 8, 2017.

The article studies the advantage of Support Vector Regression (SVR) over Simple Linear Regression (SLR) models for predicting real values, using the same basic idea as Support Vector Machines (SVM) use for classification.**What is a Support Vector Machine, and Why Would I Use it?**- Feb 23, 2017.

Support Vector Machine has become an extremely popular algorithm. In this post I try to give a simple explanation for how it works and give a few examples using the the Python Scikits libraries.**Support Vector Machines: A Concise Technical Overview**- Sep 21, 2016.

Support Vector Machines remain a popular and time-tested classification algorithm. This post provides a high-level concise technical overview of their functionality.**KDnuggets™ News 16:n25, Jul 13: Top Machine Learning MOOCs; 5 Deep Learning Projects; Support Vector Machines Overview**- Jul 13, 2016.

Top Machine Learning MOOCs and Online Lectures: A Comprehensive Overview; Support Vector Machines: A Simple Explanation; 5 Deep Learning Projects You Can No Longer Overlook; Why You Should Attend the Data Science Summit 2016 and 9 Talks To Be Excited About**Support Vector Machines: A Simple Explanation**- Jul 7, 2016.

A no-nonsense, 30,000 foot overview of Support Vector Machines, concisely explained with some great diagrams.**KDnuggets™ News 16:n24, Jul 6: Text Mining 101; Softmax and Logistic Regression; Data Mining History: Support Vector Machines**- Jul 6, 2016.

What is Softmax Regression and How is it Related to Logistic Regression; Text Mining 101: Topic Modeling; Data Mining History: The Invention of Support Vector Machines; Mining Twitter Data with Python Part 5: Data Visualisation Basics**Data Mining History: The Invention of Support Vector Machines**- Jul 4, 2016.

The story starts in Paris in 1989, when I benchmarked neural networks against kernel methods, but the real invention of SVMs happened when Bernhard decided to implement Vladimir Vapnik algorithm.**KDnuggets™ News 16:n21, Jun 15: What Big Data, Data Science tools go together? Oppys for Machine Learning Startups**- Jun 15, 2016.

What Big Data, Data Science, Deep Learning software goes together? Opportunities for Machine Learning Startups; Top NoSQL Database Engines; How Do You Identify the Right Data Scientist for Your Team?**How to Select Support Vector Machine Kernels**- Jun 13, 2016.

Support Vector Machine kernel selection can be tricky, and is dataset dependent. Here is some advice on how to proceed in the kernel selection process.**Machine Learning Key Terms, Explained**- May 25, 2016.

An overview of 12 important machine learning concepts, presented in a no frills, straightforward definition style.**Dealing with Unbalanced Classes, SVMs, Random Forests, and Decision Trees in Python**- Apr 29, 2016.

An overview of dealing with unbalanced classes, and implementing SVMs, Random Forests, and Decision Trees in Python.**KDnuggets™ News 16:n15, Apr 27: Deep Learning vs. SVMs, Random Forests; Python Guide for Data Science**- Apr 27, 2016.

When Does Deep Learning Work Better Than SVMs or Random Forests; Comprehensive Guide to Learning Python for Data Science; Top 10 IPython Notebook Tutorials for Data Science and Machine Learning; 5,000 KDnuggets Posts - Examining Our Most Popular Content**When Does Deep Learning Work Better Than SVMs or Random Forests?**- Apr 22, 2016.

Some advice on when a deep neural network may or may not outperform Support Vector Machines or Random Forests.**5 Tribes of Machine Learning: Nov 24 ACM Webinar with Pedro Domingos, moderated by Gregory Piatetsky**- Nov 10, 2015.

Prof. Pedro Domingos, a leading AI/Machine Learning researcher will talk about 5 main schools in machine learning, each with its own master algorithm, a possible universal Master Algorithm, and implications for society. KDnuggets Editor Gregory Piatetsky will moderate.**Does Deep Learning Come from the Devil?**- Oct 9, 2015.

Deep learning has revolutionized computer vision and natural language processing. Yet the mathematics explaining its success remains elusive. At the Yandex conference on machine learning prospects and applications, Vladimir Vapnik offered a critical perspective.**Top 10 Data Mining Algorithms, Explained**- May 21, 2015.

Top 10 data mining algorithms, selected by top researchers, are explained here, including what do they do, the intuition behind the algorithm, available implementations of the algorithms, why use them, and interesting applications.**The Myth of Model Interpretability**- Apr 27, 2015.

Deep networks are widely regarded as black boxes. But are they truly uninterpretable in any way that logistic regression is not?**Top KDnuggets tweets, Mar 21-23: Machine Learning in Parallel with SVM; Good Data Sets for Data Science Practice**- Mar 24, 2014.

Machine Learning in Parallel with SVM, GLM; Good Data Sets for Data Science Practice: Big enough, requires data engineering, rich; Cartoon: Why Madame Zaza, Fortune Teller, changes to Predictive Analytics; Top 45 #BigData Tools and Platforms for Developers**KDnuggets Exclusive: Interview with Yann LeCun, Deep Learning Expert, Director of Facebook AI Lab**- Feb 20, 2014.

We discuss what enabled Deep Learning to achieve remarkable successes recently, his argument with Vapnik about (deep) neural nets vs kernel (support vector) machines, and what kind of AI can we expect from Facebook.