Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Explicit Connections Between Longitudinal Data Analysis And Kernel Machines, N D. Pearce, M. P. Wand
Explicit Connections Between Longitudinal Data Analysis And Kernel Machines, N D. Pearce, M. P. Wand
Faculty of Informatics - Papers (Archive)
Two areas of research - longitudinal data analysis and kernel machines - have large, but mostly distinct, literatures. This article shows explicitly that both fields have much in common with each other. In particular, many popular longitudinal data fitting procedures are special types of kernel machines. These connections have the potential to provide fruitful cross-fertilization between longitudinal data analytic and kernel machine methodology.
Clustering, Classification And Explanatory Rules From Harmonic Monitoring Data, Ali Asheibi, David A. Stirling, Danny Sutanto, D A. Robinson
Clustering, Classification And Explanatory Rules From Harmonic Monitoring Data, Ali Asheibi, David A. Stirling, Danny Sutanto, D A. Robinson
Faculty of Informatics - Papers (Archive)
A method based on the successful AutoClass (Cheeseman & Stutz, 1996) and the Snob research programs (Wallace & Dowe, 1994); (Baxter & Wallace, 1996) has been chosen for our research work on harmonic classification. The method utilizes mixture models (McLachlan, 1992) as a representation of the formulated clusters. This research is principally based on the formation of such mixture models (typically based on Gaussian distributions) through a Minimum Message Length (MML) encoding scheme (Wallace & Boulton, 1968). During the formation of such mixture models the various derivative tools (algorithms) allow for the automated selection of the number of clusters and …
Learning Pattern Classification Tasks With Imbalanced Data Sets, Son Lam Phung, Abdesselam Bouzerdoum, Giang Hoang Nguyen
Learning Pattern Classification Tasks With Imbalanced Data Sets, Son Lam Phung, Abdesselam Bouzerdoum, Giang Hoang Nguyen
Faculty of Informatics - Papers (Archive)
This chapter is concerned with the class imbalance problem, which has been recognised as a crucial problem in machine learning and data mining. The problem occurs when there are significantly fewer training instances of one class compared to another class.
Error Correlation Between Co2 And Co As Constraint For Co2 Flux Inversions Using Satellite Data, H Wang, D J. Jacob, M Kopacz, D B. A Jones, P Suntharalingam, J A. Fisher, R Nassar, S Pawson, J E. Nielsen
Error Correlation Between Co2 And Co As Constraint For Co2 Flux Inversions Using Satellite Data, H Wang, D J. Jacob, M Kopacz, D B. A Jones, P Suntharalingam, J A. Fisher, R Nassar, S Pawson, J E. Nielsen
Faculty of Science - Papers (Archive)
Inverse modeling of CO2 satellite observations to better quantify carbon surface fluxes requires a chemical transport model (CTM) to relate the fluxes to the observed column concentrations. CTM transport error is a major source of uncertainty. We show that its effect can be reduced by using CO satellite observations as additional constraint in a joint CO2-CO inversion. CO is measured from space with high precision, is strongly correlated with CO2, and is more sensitive than CO2 to CTM transport errors on synoptic and smaller scales. Exploiting this constraint requires statistics for the CTM transport error correlation between CO2 and CO, …
Data Report: Clast Counts And Petrography Of Gravels From Site C0007, Iodp Expedition 316, Nankai Trough, Christopher L. Fergusson
Data Report: Clast Counts And Petrography Of Gravels From Site C0007, Iodp Expedition 316, Nankai Trough, Christopher L. Fergusson
Faculty of Science - Papers (Archive)
Gravel beds drilled during Integrated Ocean Drilling Program Expedition 316 at Site C0007 in the eastern Nankai Trough, ~100km offshore from the Kii Peninsula of Honshu, southwest Japan, have been investigated using 26 samples from two horizons (Cores 316-C0007C/C-17H and 316-C0007D-12R). The gravel from Core 316-C0007c-17H is from a 1.7m thick layer, whereas the gravel from Core 316-C0007D-12R is from a layer 4.55m thick. The upper parts of both layers are normally graded. The gravel is clast-supported, polymictic, of granule to pebble size, moderately to poorly sorted, and with subrounded to angular fragments. Clast types are similar from both cores …