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gptkbp:instanceOf
|
gptkb:algorithm
|
|
gptkbp:advantage
|
simple to implement
computationally expensive at prediction
no training phase
sensitive to irrelevant features
sensitive to the scale of data
|
|
gptkbp:alternativeTo
|
gptkb:tree
gptkb:naive_Bayes
support vector machine
logistic regression
|
|
gptkbp:category
|
instance-based learning
lazy learning
|
|
gptkbp:compatibleWith
|
model training
|
|
gptkbp:dependsOn
|
gptkb:Metric
feature scaling
choice of k
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|
gptkbp:fullName
|
K-Nearest Neighbors
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
KNN
|
|
gptkbp:implementedIn
|
gptkb:MATLAB
gptkb:Weka
gptkb:scikit-learn
R
|
|
gptkbp:input
|
feature vectors
|
|
gptkbp:introduced
|
Fix and Hodges
|
|
gptkbp:introducedIn
|
1951
|
|
gptkbp:measures
|
gptkb:Hamming_distance
gptkb:Manhattan_distance
gptkb:Minkowski_distance
Euclidean distance
|
|
gptkbp:output
|
class label
regression value
|
|
gptkbp:parameter
|
number of neighbors (k)
|
|
gptkbp:relatedTo
|
gptkb:Metric
gptkb:curse_of_dimensionality
feature scaling
majority voting
weighted voting
|
|
gptkbp:requires
|
labeled data
|
|
gptkbp:type
|
supervised learning
|
|
gptkbp:usedFor
|
gptkb:dictionary
regression
|
|
gptkbp:usedIn
|
gptkb:machine_learning
image recognition
pattern recognition
recommendation systems
data mining
anomaly detection
|
|
gptkbp:bfsParent
|
gptkb:KNN관객상
gptkb:Kings_Norton_railway_station
gptkb:Khanna_Railway_Station
|
|
gptkbp:bfsLayer
|
7
|