DFSSL seeks to obtain a function ( f , f ):( X , X )( Y , Y ) that can accurately predict the label ( y , y ) of sample ( x , x ) and classify it into class ( C y , C y ) with membership degree ( C y ( x ), C y ( x ) ). Here, ( x i , x i )and( x i , x ' i ) are d-dimensional vectors, ( y i , y i )( Y , Y ) is the label of sample ( x i , x i ),and| ( L , L ) |,| U , U | are the sizes of ( L , L )and( U , U ), respectively (that is, the number of samples contained in the labelled and unlabelled sample sets).

Compared with the previous definitions, ...

Get Dynamic Fuzzy Machine Learning now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.