Abstract: A method of machine learning utilizing binary classification trees combined with Bayesian classifiers in which a training component includes creating nodes, utilizing the expectation maximization algorithm to create statistical kernels and mixtures for leaf nodes and kappa statistics to identify nodes as leaf nodes or branch nodes, creating hyperplanes for branch nodes, splitting the training data for branch nodes into subsets to be provided to subtrees, and in which an operating component traverses a binary classifier tree with a feature vector to be classified by determining for each branch node whether the feature vector lies to the left or the right of the branch node hyperplane and finally classifies the feature vector upon arriving at a leaf node by computing log likelihoods for the feature vector for each mixture in the leaf node and determining the classification of the feature vector according to the highest log likelihood, in which the preferred embodiment described is classification of internal com

Abstract: An apparatus and method are provided for information transfer among software agents operating simultaneously on a digital network. A software agent is first created with an initial knowledge base and then placed into a community of agents setup on the network. Using an initial knowledge base, and utilizing built-in learning capabilities, the agent transfers information to other agents directly, or via a central community server. Information gathered by the agent can be either utilized by the agent to make decisions consistent with a pre-defined goal, or set of goals or the information may be presented to a user.

Abstract: A method and device for extrapolating past signal-history data for insertion into missing data segments in order to conceal digital speech frame errors. The extrapolation method uses past-signal history that is stored in a buffer. The method is implemented with a device that utilizes a finite-impulse response (FIR) multi-layer feed-forward artificial neural network that is trained by back-propagation for one-step extrapolation of speech compression algorithm (SCA) parameters. Once a speech connection has been established, the speech compression algorithm device begins sending encoded speech frames. As the speech frames are received, they are decoded and converted back into speech signal voltages. During the normal decoding process, pre-processing of the required SCA parameters will occur and the results stored in the past-history buffer.