Computer Associates Think, Inc.

Ca, Inc.

Baofu Duan

Yoh-Han Pao

Zhuo Meng

Ronald J Cass

Ronald Cass

A method of training a neural net includes receiving a plurality of sets of data, each set representative of a plurality of inputs to the neural net and a resulting at least one output from the neural net and calculating a plurality of network weights for the neural network based on the received plurality of sets of data. Calculating the plurality of network weights including attributing greater weight in the calculation to at least one set of the plurality of sets of data than at least one other set of the plurality of sets of data.

Weighted pattern learning for neural networks

A method for hierarchical visualization of multi-dimensional data is provided. A first dimension-reduction process is applied to a multi-dimensional data set to obtain a first visualization. A subset of the multi-dimensional data set associated with a selected region of the dimension-reduced first visualization is selected. A second dimension-reduction process is applied to the selected subset of the multi-dimensional data set to obtain at least one additional visualization.

Viewing multi-dimensional data through hierarchical visualization

An apparatus and method for processing mixed data for a selected task is provided. An input transformation module converts mixed data into converted data. A functional mapping module processes the converted data to provide a functional output for the selected task. The selected task may be one or a combination of a variety of possible tasks, including search, recall, prediction, classification, etc. For example, the selected task may be for data mining, database search, targeted marketing, computer virus detection, etc.

Processing mixed numeric and symbolic data encodings using scaling at one distance of at least one dimension, clustering, and a signpost transformation

A model maintenance method is provided. If accuracy of prediction by a current model through consultation with new data is determined to be below a predetermined threshold, a compound model is formed by supplementing the current model with a local net trained with the new data.

Automatic model maintenance through local nets

Methods for feature selection based on hierarchical local-region analysis of feature relationships in a data set are provided.

Hierarchical determination of feature relevancy

A method for feature selection based on hierarchical local-region analysis of feature characteristics in a data set of mixed data type is provided. A data space associated with a mixed-type data set is partitioned into a hierarchy of plural local regions. A relationship metric (for example, a similarity correlation metric) is used to evaluate for each local region a relationship measure between input features and a target. One or more relevant features is identified, by using the relationship measure for each local region.

Hierarchical determination of feature relevancy for mixed data types

An adaptive learning method for automated maintenance of a neural net model is provided. The neural net model is trained with an initial set of training data. Partial products of the trained model are stored. When new training data are available, the trained model is updated by using the stored partial products and the new training data to compute weights for the updated model.

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Baofu Duan