k_nearest neighbor method principle

What is KNN?
The K-nearest neighbors algorithm is a method for
classifying objects based on closest training examples in
the feature space.
KNN algorithm:
K-Nearest Neighbors (KNN) classification divides data into
a test set and a training set. For each row of the test set,
the K nearest (in Euclidean distance) training set objects
are found, and the classification is determined by majority
vote with ties broken at random. If there are ties for the Kth
nearest vector, all candidates are included in the vote.
In themodelsthatwehaveseen,weselectahypothesisspaceand
adjust axedsetofparameterswiththetrainingdata(h(x))
Weassumethattheparameters  summarizethetrainingandwecan
forgetaboutit
This methodsarecalled parametric models
When wehaveasmallamountofdataitmakessensetohaveasmall
set of parameters and to constraint the complexity of the model

When wehavealargequantityofdata,overttingislessanissue
If datashowsthatthehipothesishastobecomplex,wecantryto
adjust tothatcomplexity
A non parametric modelisonethatcannotbecharacterizedbya
xed setofparameters
A familyofnonparametricmodelsis Instance BasedLearning
Javier Bejar