Learning with Intelligent Teacher: Similarity Control and Knowledge Transfer

In the talk, I will introduce a model of learning with Intelligent Teacher. In this model, Intelligent Teacher supplies (some) training examples $\mathscr{(x_i, y_i), i=1, \dots , l, x_i \in X,y_i \in \{-1,1\}}$ with additional (privileged) information) $\mathscr{x_i^* \in X^*}$ forming training triplets $\mathscr (x_i,x_i^*, y_i), i, \dots , l$. Privileged information is available only for training examples and $not\, available\, for\, text\, examples$. Using privileged information it is required to find a better training processes (that use less examples or more accurate with the same number of examples) than the classical ones. In this lecture, I will present two additional mechanisms that exist in learning with Intelligent Teacher * The mechanism to control Student's concept of examples similarity and * The mechanism to transfer knowledge that can be obtained in space of privileged information to the desired space of decision rules. Privileged information exists for many inference problem and Student-Teacher interaction can be considered as the basic element of intelligent behavior.