文件名称:Kode-Program-Algoritma-Nearest-Neighbor

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In pattern recognition, the k-nearest neighbor algorithm (k-NN) is a method for classifying objects based on closest training examples in the feature space. k-NN is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is deferred until classification. The k-nearest neighbor algorithm is amongst the simplest of all machine learning algorithms: an object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of its nearest neighbor.
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下载文件列表

Similarity.~dpr

Similarity.cfg

Similarity.dof

Similarity.dpr

Similarity.exe

SIMILARITY.GDB

Similarity.res

UDM.~ddp

UDM.~dfm

UDM.~pas

UDM.dcu

UDM.ddp

UDM.dfm

UDM.pas

UInputKasus.~ddp

UInputKasus.~dfm

UInputKasus.~pas

UInputKasus.dcu

UInputKasus.ddp

UInputKasus.dfm

UInputKasus.pas

UKasus.~dfm

UKasus.~pas

UKasus.dcu

UKasus.dfm

UKasus.pas

UNilaiVariabel.~ddp

UNilaiVariabel.~dfm

UNilaiVariabel.~pas

UNilaiVariabel.dcu

UNilaiVariabel.ddp

UNilaiVariabel.dfm

UNilaiVariabel.pas

USettingAtribut.~ddp

USettingAtribut.~dfm

USettingAtribut.~pas

USettingAtribut.dcu

USettingAtribut.ddp

USettingAtribut.dfm

USettingAtribut.pas

UTesting.~ddp

UTesting.~dfm

UTesting.~pas

UTesting.dcu

UTesting.ddp

UTesting.dfm

UTesting.pas

UUtama.~ddp

UUtama.~dfm

UUtama.~pas

UUtama.dcu

UUtama.ddp

UUtama.dfm

UUtama.pas

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