Difference between revisions of "Radial Basis Function Network (RBFN)"
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Performs classification by measuring the input’s similarity to examples from the training set. Each RBFN neuron stores a “prototype”, which is just one of the examples from the training set. When we want to classify a new input, each neuron computes the Euclidean distance between the input and its prototype. Roughly speaking, if the input more closely resembles the class A prototypes than the class B prototypes, it is classified as class A. [http://mccormickml.com/2013/08/15/radial-basis-function-network-rbfn-tutorial/ Radial Basis Function Network (RBFN) Tutorial | Chris McCormick] | Performs classification by measuring the input’s similarity to examples from the training set. Each RBFN neuron stores a “prototype”, which is just one of the examples from the training set. When we want to classify a new input, each neuron computes the Euclidean distance between the input and its prototype. Roughly speaking, if the input more closely resembles the class A prototypes than the class B prototypes, it is classified as class A. [http://mccormickml.com/2013/08/15/radial-basis-function-network-rbfn-tutorial/ Radial Basis Function Network (RBFN) Tutorial | Chris McCormick] | ||
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| + | Note: [[Support Vector Machine (SVM)]] represent a special case of RBFNs. | ||
As a non-linear classifier... | As a non-linear classifier... | ||
Revision as of 16:15, 28 June 2018
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Performs classification by measuring the input’s similarity to examples from the training set. Each RBFN neuron stores a “prototype”, which is just one of the examples from the training set. When we want to classify a new input, each neuron computes the Euclidean distance between the input and its prototype. Roughly speaking, if the input more closely resembles the class A prototypes than the class B prototypes, it is classified as class A. Radial Basis Function Network (RBFN) Tutorial | Chris McCormick
Note: Support Vector Machine (SVM) represent a special case of RBFNs.
As a non-linear classifier...
Adversarial Attack
Watch 10:15 into the following video...
