Chandrika Kamath - Tracy CA, US Erick Cantu-Paz - Oakland CA, US David Littau - Minneapolis MN, US
Assignee:
The Regents of the University of California - Oakland CA
International Classification:
G06F017/30
US Classification:
707 6
Abstract:
A system for decision tree ensembles that includes a module to read the data, a module to create a histogram, a module to evaluate a potential split according to some criterion using the histogram, a module to select a split point randomly in an interval around the best split, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method includes the steps of reading the data; creating a histogram; evaluating a potential split according to some criterion using the histogram, selecting a split point randomly in an interval around the best split, splitting the data, and combining multiple decision trees in ensembles.
Parallel Object-Oriented, Denoising System Using Wavelet Multiresolution Analysis
Chandrika Kamath - Dublin CA, US Chuck H. Baldwin - Dublin CA, US Imola K. Fodor - Oakland CA, US Nu A. Tang - Sacramento CA, US
Assignee:
The Regents of the University of California - Oakland OH
International Classification:
G06K009/40 G06K009/54 G06K009/60
US Classification:
382254, 382304, 382260, 382275
Abstract:
The present invention provides a data de-noising system utilizing processors and wavelet denoising techniques. Data is read and displayed in different formats. The data is partitioned into regions and the regions are distributed onto the processors. Communication requirements are determined among the processors according to the wavelet denoising technique and the partitioning of the data. The data is transforming onto different multiresolution levels with the wavelet transform according to the wavelet denoising technique, the communication requirements, and the transformed data containing wavelet coefficients. The denoised data is then transformed into its original reading and displaying data format.
Creating Ensembles Of Decision Trees Through Sampling
Chandrika Kamath - Tracy CA, US Erick Cantu-Paz - Oakland CA, US
Assignee:
The Regents of the University of California - Oakland CA
International Classification:
G06F017/30
US Classification:
707102, 706 14, 705 7
Abstract:
A system for decision tree ensembles that includes a module to read the data, a module to sort the data, a module to evaluate a potential split of the data according to some criterion using a random sample of the data, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method is based on statistical sampling techniques and includes the steps of reading the data; sorting the data; evaluating a potential split according to some criterion using a random sample of the data, splitting the data, and combining multiple decision trees in ensembles.
Chandrika Kamath - Dublin CA, US Erick Cantu-Paz - Oakland CA, US
Assignee:
The Regents of the University of California - Oakland CA
International Classification:
G06F 17/30
US Classification:
707102, 706 45, 712 13
Abstract:
A data mining decision tree system that uncovers patterns, associations, anomalies, and other statistically significant structures in data by reading and displaying data files, extracting relevant features for each of the objects, and using a method of recognizing patterns among the objects based upon object features through a decision tree that reads the data, sorts the data if necessary, determines the best manner to split the data into subsets according to some criterion, and splits the data.
Creating Ensembles Of Oblique Decision Trees With Evolutionary Algorithms And Sampling
Erick Cantu-Paz - Oakland CA, US Chandrika Kamath - Tracy CA, US
Assignee:
The Regents of the University of California - Oakland CA
International Classification:
G06F 17/00
US Classification:
707102, 706 20, 706 45
Abstract:
A decision tree system that is part of a parallel object-oriented pattern recognition system, which in turn is part of an object oriented data mining system. A decision tree process includes the step of reading the data. If necessary, the data is sorted. A potential split of the data is evaluated according to some criterion. An initial split of the data is determined. The final split of the data is determined using evolutionary algorithms and statistical sampling techniques. The data is split. Multiple decision trees are combined in ensembles.
Chandrika Kamath - Dublin CA, US Erick Cantu-Paz - Oakland CA, US
Assignee:
The Regents of the University of California
International Classification:
G06F007/00
US Classification:
707/002000
Abstract:
A data mining system uncovers patterns, associations, anomalies and other statistically significant structures in data. Data files are read and displayed. Objects in the data files are identified. Relevant features for the objects are extracted. Patterns among the objects are recognized based upon the features. Data from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) sky survey was used to search for bent doubles. This test was conducted on data from the Very Large Array in New Mexico which seeks to locate a special type of quasar (radio-emitting stellar object) called bent doubles. The FIRST survey has generated more than 32,000 images of the sky to date. Each image is 7.1 megabytes, yielding more than 100 gigabytes of image data in the entire data set.
Chandrika Kamath - Dublin CA, US Erick Cantu-Paz - Oakland CA, US
International Classification:
G06F007/00
US Classification:
70710300R
Abstract:
A data mining decision tree system that uncovers patterns, associations, anomalies, and other statistically significant structures in data by reading and displaying data files, extracting relevant features for each of the objects, and using a method of recognizing patterns among the objects based upon object features through a decision tree that reads the data, sorts the data if necessary, determines the best manner to split the data into subsets according to some criterion, and splits the data.
A video camera produces a video sequence including moving objects. A computer is adapted to process the video sequence, produce individual frames, and use a fast-adapting background subtraction model to validate the results of a slow-adapting background subtraction model to improve identification of the moving objects.