Support Vector Machine (SVM) - Revision history

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BPeat at 15:13, 23 February 2020

2020-02-23T15:13:55Z

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 [http://www.google.com/search?q=support+vector+machines+SVM+machine+learning+ML ...Google search]
-* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
 ** [[...predict categories]]
 * [[One-class Support Vector Machine (SVM)]]

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 * [https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-locally-deep-support-vector-machine Two-Class Locally Deep Support Vector Machine | Microsoft]
-Support vector machines (SVMs) find the boundary that separates classes by as wide a margin as possible. When the two classes can't be clearly separated, the algorithms find the best boundary they can. As written in Machine Learning, the two-class SVM does this with a straight line only. (In SVM-speak, it uses a linear kernel.) Because it makes this linear approximation, it is able to run fairly quickly. Where it really shines is with feature-intense data, like text or genomic. In these cases SVMs are able to separate classes more quickly and with less overfitting than most other algorithms, in addition to requiring only a modest amount of memory. - Dinesh Chandrasekar
+Support vector machines (SVMs) find the boundary that separates classes by as wide a margin as possible. When the two classes can't be clearly separated, the algorithms find the best boundary they can. As written in Machine Learning, the two-class SVM does this with a straight line only. (In SVM-speak, it uses a linear kernel.) Because it makes this linear approximation, it is able to run fairly quickly. Where it really shines is with feature-intense data, like text or genomic. In these cases SVMs are able to separate classes more quickly and with less overfitting than most other algorithms, in addition to requiring only a modest amount of [[memory]]. - Dinesh Chandrasekar
 <youtube>eOwlXaDtwps</youtube>

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 [http://www.google.com/search?q=support+vector+machines+SVM+machine+learning+ML ...Google search]
-* [[AI Solver]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
 ** [[...predict categories]]
 * [[One-class Support Vector Machine (SVM)]]

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 * [http://www.giocc.com/harnessing-the-grid-ai-with-support-vector-machines.html Harnessing The Grid AI with Support Vector Machines | Gio Carlo Cielo]
 * [[Feature Exploration/Learning]]
-* [[Math for Intelligence]] ... [[Finding Paul Revere]]
+* [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]]
 __________________________________________________________

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 |title=PRIMO.ai
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-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
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 [http://www.youtube.com/results?search_query=support+vector+machine+tensorflow YouTube search...]

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 __________________________________________________________
-Support vector machines (SVM) find optimal solutions for classification problems. Classically they were only capable of categorising linearly separable data; say finding which images are of Garfield and which of Snoopy, with any other outcome not being possible. During training, SVMs can be thought of as plotting all the data (Garfields and Snoopys) on a graph (2D) and figuring out how to draw a line between the data points. This line would separate the data, so that all Snoopys are on one side and the Garfields on the other. This line moves to an optimal line in such a way that the margins between the data points and the line are maximised on both sides. Classifying new data would be done by plotting a point on this graph and simply looking on which side of the line it is (Snoopy side or Garfield side). Using the kernel trick, they can be taught to classify n-dimensional data. This entails plotting points in a 3D plot, allowing it to distinguish between Snoopy, Garfield AND Simon’s cat, or even higher dimensions distinguishing even more cartoon characters. SVMs are not always considered neural networks. Cortes, Corinna, and Vladimir Vapnik. “Support-vector networks.” Machine learning 20.3 (1995): 273-297.
+Support vector machines (SVM) find optimal solutions for classification problems. [But can be used for regression as well]  Classically they were only capable of categorising linearly separable data; say finding which images are of Garfield and which of Snoopy, with any other outcome not being possible. During training, SVMs can be thought of as plotting all the data (Garfields and Snoopys) on a graph (2D) and figuring out how to draw a line between the data points. This line would separate the data, so that all Snoopys are on one side and the Garfields on the other. This line moves to an optimal line in such a way that the margins between the data points and the line are maximised on both sides. Classifying new data would be done by plotting a point on this graph and simply looking on which side of the line it is (Snoopy side or Garfield side). Using the kernel trick, they can be taught to classify n-dimensional data. This entails plotting points in a 3D plot, allowing it to distinguish between Snoopy, Garfield AND Simon’s cat, or even higher dimensions distinguishing even more cartoon characters. SVMs are not always considered neural networks. Cortes, Corinna, and Vladimir Vapnik. “Support-vector networks.” Machine learning 20.3 (1995): 273-297.
 Note: SVMs represent a special case of [[Radial Basis Function Network (RBFN)]].

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 * [[Support Vector Regression (SVR)]]
 * [[Metric Learning and Kernel Learning]]
 * [http://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen]
 * [http://www.giocc.com/harnessing-the-grid-ai-with-support-vector-machines.html Harnessing The Grid AI with Support Vector Machines | Gio Carlo Cielo]