Umsebenzi weRadial Basis Function (RBF) Kernel: Ukuya kwiKernel

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Usebenza kumatshini wokufunda ialgorithm njengeNkxaso yoomatshini beVekethi yedatha engeyiyo yomgca kwaye akunakubonakala ngathi ungafumana olona phawu luguquguqukayo okanye i-kernel efanelekileyo oyisebenzisayo. Ewe, ungoyiki kuba iRadial Basis Function (RBF) Kernel ingumsindisi wakho.



Iinkozo ze-RBF zezona ndlela ziqhelekileyo zokwenza i-kernelization kwaye yenye yezona zisetyenziswa kakhulu ngenxa yokufana kwayo nokusasazwa kweGaussian. Umsebenzi we-RBF kernel wamanqaku amabini u-X kunye no-X₂ ubala ukufana okanye ukuba zikufuphi kangakanani omnye komnye. Le kernel inokumelwa ngokwemathematika ngolu hlobo lulandelayo:

Umfanekiso weposi



phi,

1. 'σ' ngumahluko kunye neparamparitha yethu



2. || _X₁ - X₂ || nguEuclidean (L_₂ -norm) Umgama phakathi kwamanqaku amabini u-X₁ no-X₂

Masibe ngumgama phakathi kwala manqaku mabini u-X₁ no-X₂, ngoku sinokumela ngolu hlobo lulandelayo:

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Umfanekiso weposi

Umzobo 2: Umgama phakathi kwamanqaku amabini esithubeni [Umfanekiso nguMbhali]

I-kernel equation ingabhalwa kwakhona ngolu hlobo lulandelayo:

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Umfanekiso weposi

Elona xabiso liphezulu linokuba yi-RBF kernel ngu-1 kwaye yenzeka xa d₁₂ ingu-0 kulapho amanqaku afanayo, okt. X₁ = X₂.

  1. Xa amanqaku efana, akukho mgama phakathi kwawo kwaye ke ayafana kakhulu
  2. Xa amanqaku ahlukaniswe ngumgama omkhulu, ixabiso le-kernel lingaphantsi kwe-1 kwaye lisondele ku-0 nto leyo ethetha ukuba amanqaku awafani.

Umgama unokucingelwa ulingane nokungafani kuba sinokuthi siqaphele ukuba xa umgama phakathi kwamanqaku unyuka, awufani ncam.

# intelligence-artificial #analytics # support-vector-machine # data-science # machine-learning

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Umsebenzi weRadial Basis Function (RBF) Kernel: Ukuya kwiKernel

Ekufundeni komatshini, isiseko se-radial function kernel, okanye i-RBF kernel, ngumsebenzi owaziwayo we-kernel osetyenziswa ngeendlela ezahlukeneyo zokufunda ze-kernelised algorithms. Ngokukodwa, ihlala isetyenziselwa ukuxhasa ukuhlelwa komatshini wevektha.