The Relationship between Decision Trees and the Scale of Train Data Set

Decision Trees need train samples in the train data set to get classification rules. If the number of train data was too small, the important information might be missed and thus the model could not explain the classification rules of data. While it is not affirmative that large scale of train data set can get well model. This Paper analysis the relationship between decision trees and the train data scale. We use nine decision tree algorithms to experiment the accuracy, complexity and robustness of decision tree algorithms. Some results are demonstrated.

Spatio-Temporal Scanning and Statistical Test of the Accelerating Moment Release (AMR) Model Using Australian Earthquake Data

The Accelerating Moment Release (AMR) preceding earthquakes with magnitude above 5 in Australia that occurred during the last 20 years was analyzed to test the Critical Point Hypothesis. Twelve earthquakes in the catalog were chosen based on a criterion for the number of nearby events. Results show that seven sequences with numerous events recorded leading up to the main earthquake exhibited accelerating moment release. Two occurred near in time and space to other earthquakes preceded by AM R. The remaining three sequences had very few events in the catalog so the lack of AMR detected in the analysis may be related to catalog incompleteness. Spatio-temporal scanning of AMR parameters shows that 80% of the areas in which AMR occurred experienced large events. In areas of similar background seismicity with no large events, 10 out of 12 cases exhibit no AMR, and two others are false alarms where AMR was observed but no large event followed. The relationship between AMR and Load-Unload Response Ratio (LURR) was studied. Both methods predict similar critical region sizes, however, the critical point time using AMR is slightly earlier than the time of the critical point LURR anomaly.

A combined statistical model for multiple motifs search

Transcription factor binding sites (TFBS) play key roles in genebior 6.8 wavelet expression and regulation. They are short sequence segments with de¯nite structure and can be recognized by the corresponding transcription factors correctly. From the viewpoint of statistics, the candidates of TFBS should be quite di®erent from the segments that are randomly combined together by nucleotide. This paper proposes a combined statistical model for ¯nding over- represented short sequence segments in di®erent kinds of data set. While the over-represented short sequence segment is described by position weight matrix, the nucleotide distribution at most sites of the segment should be far from the background nucleotide distribution. The central idea of this approach is to search for such kind of signals. This algorithm is tested on 3 data sets, including binding sites data set of cyclic AMP receptor protein in E.coli, PlantProm DB which is a non-redundant collection of proximal promoter sequences from di®erent species, collection of the intergenic sequences of the whole genome of E.Coli. Even though the complexity of these three data sets is quite di®erent, the results show that this model is rather general and sensible.

Constrained high-order statistical blind deconvolution of spectral data

A constrained high-order statistical algorithm is proposed to blindly deconvolute the measured spectral data and estimate the response function of the instruments simultaneously. In this algorithm, no prior-knowledge is necessary except a proper length of the unit-impulse response. This length can be easily set to be the width of the narrowest spectral line by observing the measured data. The feasibility of this method has been demonstrated experimentally by the measured Raman and absorption spectral data.

Statistical approach to bulk inclusion initialized damage in films

In experiments, we have found an abnormal relationship between probability of laser induced damage and number density of surface inclusion. From results of X-ray diffraction (XRD) and laser induced damage, we have drawn a conclusion that bulk inclusion plays a key role in damage process. Combining thermo-mechanical damage process and statistics of inclusion density distribution, we have deduced an equation which reflects the relationship between probability of laser induced damage, number density of inclusion, power density of laser pulse, and thickness of films. This model reveals that relationship between critical sizes of the dangerous inclusions (dangerous inclusions refer to the inclusions which can initialize film damage), embedded depth of inclusions, thermal diffusion length and tensile strength of films. This model develops the former work which is the statistics about surface inclusion. (c) 2006 Elsevier B.V. All rights reserved.