5. The learning machine defined in claim 1 , wherein said linear combination is a sum of said label of said second feature vector and said vector with components all equal to 1.
1. A learning machine for processing exogenous feature vectors to recognize patterns, each exogenous feature vector being a feature vector input to said learning machine, said learning machine comprising at least one processing unit that comprises (a) expansion means for generating, in response to a first feature vector input to said processing unit, a first orthogonal expansion of a subvector of said first feature vector, said first orthogonal expansion comprising components of said subvector of said first feature vector and a plurality of products of said components of said subvector of said first feature vector; (b) at least one first expansion correlation matrix that is a weighted sum of outer products, each being an outer product of a linear combination of a vector with components all equal to 1 and a label of a second feature vector input to said processing unit and a second orthogonal expansion of a subvector of said second feature vector, said second orthogonal expansion comprising components of said subvector of said second feature vector and a plurality of products of said components of said subvector of said second feature vector; and (c) estimation means for using at least said at least one first expansion correlation matrix and at least one third orthogonal expansion of a subvector of a third feature vector input to said processing unit, said third orthogonal expansion being generated by said expansion means in response to said third feature vector, to compute a representation of a probability distribution of a label of said third feature vector.