Abstract: In a single-signature duplicate document system, a secondary set of attributes is used in addition to a primary set of attributes so as to improve the precision of the system. When the projection of a document onto the primary set of attributes is below a threshold, then a secondary set of attributes is used to supplement the primary lexicon so that the projection is above the threshold.

Abstract: In a single-signature duplicate document system, a secondary set of attributes is used in addition to a primary set of attributes so as to improve the precision of the system. When the projection of a document onto the primary set of attributes is below a threshold, then a secondary set of attributes is used to supplement the primary lexicon so that the projection is above the threshold.

Abstract: A probabilistic classifier is used to classify data items in a data stream. The probabilistic classifier is trained, and an initial classification threshold is set, using unique training and evaluation data sets (i.e., data sets that do not contain duplicate data items). Unique data sets are used for training and in setting the initial classification threshold so as to prevent the classifier from being improperly biased as a result of similarity rates in the training and evaluation data sets that do not reflect similarity rates encountered during operation. During operation, information regarding the actual similarity rates of data items in the data stream is obtained and used to adjust the classification threshold such that misclassification costs are minimized given the actual similarity rates.

Abstract: A probabilistic classifier is used to classify data items in a data stream. The probabilistic classifier is trained, and an initial classification threshold is set, using unique training and evaluation data sets (i.e., data sets that do not contain duplicate data items). Unique data sets are used for training and in setting the initial classification threshold so as to prevent the classifier from being improperly biased as a result of similarity rates in the training and evaluation data sets that do not reflect similarity rates encountered during operation. During operation, information regarding the actual similarity rates of data items in the data stream is obtained and used to adjust the classification threshold such that misclassification costs are minimized given the actual similarity rates.

Abstract: An e-mail filter is used to classify received e-mails so that some of the classes may be filtered, blocked, or marked. The e-mail filter may include a classifier that can classify an e-mail as belonging to a particular class and an e-mail grouper that can detect substantially similar, but possibly not identical, e-mails. The e-mail grouper determines groups of substantially similar e-mails in an incoming e-mail stream. For each group, the classifier determines whether one or more test e-mails from the group belongs to the particular class. The classifier then designates the class to which the other e-mails in the group belong based on the results for the test e-mails.

Abstract: A channel equalizer is formed using a self-learning neural network. During a training period, the neural network is taught the channel response function. The network is then used to equalize distortions introduced into signals by the channel. The neural network may be a Boltzmann Machine type of neural network comprising neurons arranged in an input layer, a hidden layer, and an output layer. The neurons are interconnected by bidirectional symmetric weighted synapses. Each neuron is preferably implemented by an analog integrated circuit. Direct communication between the input and output layers helps in faster channel acquisition. The scheme can very easily be extended to multilevel and multisymbol modulation schemes such as QAM and PSK.

Abstract: A neuron for use in a self-learning neural network comprises a current input node at which a plurality of synaptic input currents are summed using Kirchoff's current law. The summed input currents are normalized using a coarse gain current normalizer. The normalized summed inputs current is then converted to a voltage using a current to voltage converter. This voltage is then amplified by a gain controlled cascode output amplifier. Gain control inputs are provided in the output amplifier so that the neuron can be settled by the Mean Field Approximation. A noise input stage is also connected to the output amplifier so that the neuron can be settled using simulated annealing. The resulting neuron is a variable gain, bi-directional current transimpedance neuron with a controllable noise input.

Abstract: A neuron network which achieves learning by means of a modified Boltzmann algorithm. The network may comprise interconnected input, hidden and output layers of neurons, the neurons being "on-off" or threshold electronic devices which are symmetrically connected by means of adjustable-weight synapse pairs. The synapses comprise the source-drain circuits of a plurality of paralleled FETs which differ in resistance or conductance in a binary sequence. The synapses are controlled by the output of an Up-Down counter, the reading of which is controlled by the results of a correlation of the states of the two neurons connected by the synapse pairs following the application of a set of plus and minus training signals to selected neurons of said network. A noise generator comprising a thermal noise source is provided for each neuron for the purpose of simulated annealing of the network.

Abstract: Polyoxide capacitors for semiconductor integrated circuits having oxide dielectric films of 500 Angstroms or less are fabricated using in-situ doped polysilicon layers to have electrical field breakdowns of from 6 to 9 MV/cm. The first polysilicon layer is formed by LPCVD using silane and phosphine at a temperature in the range from about 570 degrees C. to 595 degrees C.These capacitors are relatively precisely valued devices used particularly in applications such as filter/codecs. However, they are useful wherever integral capacitors are needed having high dielectric strength polyoxides, including such semiconductor integrated circuit devices as EPROMs and dynamic RAMs.