Matching of catalogues by probabilistic pattern classification

We consider the statistical problem of catalogue matching from a machine learning perspective with the goal of producing probabilistic outputs, and using all available information. A framework is provided that unifies two existing approaches to producing probabilistic outputs in the literature, one based on combining distribution estimates and the other based on combining probabilistic classifiers. We apply both of these to the problem of matching the HI Parkes All Sky Survey radio catalogue with large positional uncertainties to the much denser SuperCOSMOS catalogue with much smaller positional uncertainties. We demonstrate the utility of probabilistic outputs by a controllable completeness and efficiency trade-off and by identifying objects that have high probability of being rare. Finally, possible biasing effects in the output of these classifiers are also highlighted and discussed.

Batting with occluded vision: An in situ examination of the information pick-up and interceptive skills of high- and low-skilled cricket batsmen

The capability of cricket batsmen of different skill levels to pick-up information from the pre-release movement pattern of the bowler, from pre-bounce ball flight, and from post-bounce ball flight was examined experimentally. Six highly skilled and six low-skilled cricket batsmen batted against three different leg-spin bowlers while wearing liquid crystal spectacles. The spectacles permitted the specific information available to the batsmen on each trial to be manipulated such that vision was either: (i) occluded at a point prior to the point of ball release (thereby only allowing vision of advance information from the bowler's delivery action); (ii) occluded at a point prior to the point of bat[ bounce (thereby permitting the additional vision of pre-bounce ball flight); or (iii) not occluded (thereby permitting the additional vision of post-bounce bat[ flight information). Measurement was made on each trial of both the accuracy of the definitive (forward-backward) foot movements made by the batsmen and their success (or otherwise) in making bat-bat[ contact. The analyses revealed a superior capability of the more skilled players to make use of earlier (pre-bounce) bat[ flight information to guide successful bat-bat[ interception, thus mirroring the greater use of prospective information pick-up by skilled performers observed in other aspects of batting and in other time-constrained performance domains. (c) 2006 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Discovering user access pattern based on probabilistic latent factor model

There has been an increased demand for characterizing user access patterns using web mining techniques since the informative knowledge extracted from web server log files can not only offer benefits for web site structure improvement but also for better understanding of user navigational behavior. In this paper, we present a web usage mining method, which utilize web user usage and page linkage information to capture user access pattern based on Probabilistic Latent Semantic Analysis (PLSA) model. A specific probabilistic model analysis algorithm, EM algorithm, is applied to the integrated usage data to infer the latent semantic factors as well as generate user session clusters for revealing user access patterns. Experiments have been conducted on real world data set to validate the effectiveness of the proposed approach. The results have shown that the presented method is capable of characterizing the latent semantic factors and generating user profile in terms of weighted page vectors, which may reflect the common access interest exhibited by users among same session cluster.

A design pattern for efficient retrieval of large data sets from remote data sources

Retrieving large amounts of information over wide area networks, including the Internet, is problematic due to issues arising from latency of response, lack of direct memory access to data serving resources, and fault tolerance. This paper describes a design pattern for solving the issues of handling results from queries that return large amounts of data. Typically these queries would be made by a client process across a wide area network (or Internet), with one or more middle-tiers, to a relational database residing on a remote server. The solution involves implementing a combination of data retrieval strategies, including the use of iterators for traversing data sets and providing an appropriate level of abstraction to the client, double-buffering of data subsets, multi-threaded data retrieval, and query slicing. This design has recently been implemented and incorporated into the framework of a commercial software product developed at Oracle Corporation.

Introducing uncertainty into pattern discovery in temporal event sequences

Pattern discovery in temporal event sequences is of great importance in many application domains, such as telecommunication network fault analysis. In reality, not every type of event has an accurate timestamp. Some of them, defined as inaccurate events may only have an interval as possible time of occurrence. The existence of inaccurate events may cause uncertainty in event ordering. The traditional support model cannot deal with this uncertainty, which would cause some interesting patterns to be missing. A new concept, precise support, is introduced to evaluate the probability of a pattern contained in a sequence. Based on this new metric, we define the uncertainty model and present an algorithm to discover interesting patterns in the sequence database that has one type of inaccurate event. In our model, the number of types of inaccurate events can be extended to k readily, however, at a cost of increasing computational complexity.

Proceedings of the Eighth Australian and New Zealand Intelligent Information Systems Conference

These are the full proceedings of the conference.