- Support Vector Machine for Hand Written Alphabet Recognition in R - Jan 27, 2021.
We attempt to break down a problem of hand written alphabet image recognition into a simple process rather than using heavy packages. This is an attempt to create the data and then build a model using Support Vector Machines for Classification.
Classification, Image Recognition, Machine Learning, R, Support Vector Machines
A Top Machine Learning Algorithm Explained: Support Vector Machines (SVM) - Mar 18, 2020.
Support Vector Machines (SVMs) are powerful for solving regression and classification problems. You should have this approach in your machine learning arsenal, and this article provides all the mathematics you need to know -- it's not as hard you might think.
Algorithms, Explained, Linear Algebra, Machine Learning, Support Vector Machines, SVM
- A Friendly Introduction to Support Vector Machines - Sep 12, 2019.
This article explains the Support Vector Machines (SVM) algorithm in an easy way.
Algorithms, Explained, Machine Learning, Support Vector Machines, SVM
- 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.
Pages: 1 2 3
Algorithms, Machine Learning, Statsbot, 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.
Data Analysis, Data Preparation, Machine Learning, Support Vector Machines, SVM, 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.
R, Regression, Support Vector Machines
- 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.
Python, scikit-learn, Support Vector Machines, SVM, Yhat
- 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.
Algorithms, Machine Learning, Support Vector Machines
- 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.
Aylien, Explanation, Machine Learning, Support Vector Machines
- 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.
History, Isabelle Guyon, Support Vector Machines, SVM, Vladimir Vapnik
- 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, Support Vector Machines
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.
Advice, Deep Learning, random forests algorithm, Support Vector Machines, SVM
- 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.
Berlin, Deep Learning, Machine Learning, Support Vector Machines, SVM, Vladimir Vapnik, Yandex, Zachary Lipton
- 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.
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Algorithms, Apriori, Bayesian, Boosting, C4.5, CART, Data Mining, Explained, K-means, K-nearest neighbors, Naive Bayes, Page Rank, Support Vector Machines, Top 10
- 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?
Deep Learning, Deep Neural Network, Interpretability, Support Vector Machines, Zachary Lipton
- 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.
Andrew Ng, Deep Learning, Facebook, Interview, NYU, Support Vector Machines, Vladimir Vapnik, Yann LeCun
