Patent ID: 6421654
Filing Date: 2002-07-16
Classification: G06K,G06N

Abstract:
Process for learning from an examples base composed of known input data and targets corresponding to the class of each of these input data, to sort objects into two distinct classes separated by at least one quadratic type or quadratic and linear type separating surface, this process consisting of generating a network of binary type neurons, each comprising parameters describing the separating surface that they determine, this neural network comprising network inputs and a layer of hidden neurons connected to these inputs and to a network output neuron characterized in that it comprises:A) An initialization step consisting of: Aa) making a choice of a type of a first neuron that is connected to inputs; Ab) learning from an examples base by this first neuron, in order to determine the descriptive parameters for a first separating surface, for this neuron; Ac) determining a number of learning errors, Ad) if this number of errors is zero, learning is finished and the first neuron chosen in Aa) becomes the network output neuron; and Ae) if this number is not zero, the parameters on the first neuron are fixed and a second neuron becomes the first neuron in a layer of hidden neurons built by: B) a step in which the hidden layer is built and the network output neuron is determined, consisting of: B1) adaptation of the layer of hidden neurons as a function of sorting to be done, consisting of: B1a) determining new targets for the examples base as a function of learning errors by a last neuron learned, the inputs in the examples base being used with new targets forming a new examples base; B1b) incrementing a hidden neuron counter by one unit, and connecting a new hidden neuron of a chosen type on the network inputs, and learning to sort the new examples base; B1c) fixing the parameters of this new neuron, states of the hidden neurons corresponding to each input data in the examples base forming an internal representation of this input data; and B2) validating the layer of hidden neurons and determining the network output neuron; and C) Using the network to sort objects.