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Field of Research : Other Artificial Intelligence
Field of Research : Pattern Recognition
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  • Funded Activity

    Discovery Projects - Grant ID: DP0208969

    Funder
    Australian Research Council
    Funding Amount
    $258,752.00
    Summary
    Kernel and Margin Based Machine Learning Algorithms. Certain machine learning algorithms, such as support vector machines, utilizing the ideas of large margins and kernels have attracted much attention lately because of their impressive performance on real world problems such as optical character recognition. We plan to refine and extend such algorithms to a wide range of different machine learning problems such as gene sequence analysis, image processing and text classification. Expected .... Kernel and Margin Based Machine Learning Algorithms. Certain machine learning algorithms, such as support vector machines, utilizing the ideas of large margins and kernels have attracted much attention lately because of their impressive performance on real world problems such as optical character recognition. We plan to refine and extend such algorithms to a wide range of different machine learning problems such as gene sequence analysis, image processing and text classification. Expected outcomes include the development of software that allows the solution of hitherto unsolved machine learning problems, and the ability to solve problems larger than those solvable by the current generation of machine learning tools.
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    Funded Activity

    Discovery Projects - Grant ID: DP0343610

    Funder
    Australian Research Council
    Funding Amount
    $277,440.00
    Summary
    Pattern Recognition and Scene Analysis via Machine Learning. We plan to use kernel methods, a novel machine learning technique, for computer vision problems, such as scene analysis and real time object recognition. Such capabilities are relevant for the design of intelligent and adaptive systems, suitable for complex real world environments. Expected outcomes are the design of efficient statistical tools which take the special nature of visual data into account (structure, decomposition, prior .... Pattern Recognition and Scene Analysis via Machine Learning. We plan to use kernel methods, a novel machine learning technique, for computer vision problems, such as scene analysis and real time object recognition. Such capabilities are relevant for the design of intelligent and adaptive systems, suitable for complex real world environments. Expected outcomes are the design of efficient statistical tools which take the special nature of visual data into account (structure, decomposition, prior knowledge of physical environments, etc.) and combine the advantages of feature based high-level vision methods with low-level machine learning techniques. This proposal is part of a joint IST project with partners from the European Union.
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    Funded Activity

    Discovery Projects - Grant ID: DP1095725

    Funder
    Australian Research Council
    Funding Amount
    $300,000.00
    Summary
    Reconceiving Machine Learning. The proposed research will develop a new way to consider problems to which machine learning can be applied. Machine learning is crucial enabler of the digital economy. The research will provide better opportunities for Australian industry to gain a competitive advantage with machine learning technology. The framework developed will enable better opportunities for collaborative research and will build and strengthen international linkages.
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    Funded Activity

    Discovery Projects - Grant ID: DP0451129

    Funder
    Australian Research Council
    Funding Amount
    $195,000.00
    Summary
    Privacy Preserving Data Mining as Autonomous Data Analysis Expands into Safeguarding from Threats like Crime and Terrorism. In a world characterized by digitally coded data, Data Mining allows automatic exploration of huge numbers of personal records for target marketing, as well as demographic, medical and criminal research. Investigations into terrorist attacks use this technology, but a balance with privacy protection is necessary. Even if names and unique identifiers are removed, computer me .... Privacy Preserving Data Mining as Autonomous Data Analysis Expands into Safeguarding from Threats like Crime and Terrorism. In a world characterized by digitally coded data, Data Mining allows automatic exploration of huge numbers of personal records for target marketing, as well as demographic, medical and criminal research. Investigations into terrorist attacks use this technology, but a balance with privacy protection is necessary. Even if names and unique identifiers are removed, computer methods can be used to infer confidential information about individuals. This project develops new techniques to ensure privacy and alleviate public concerns such as secondary use of personal data. We shall develop new methods for replacing original data with data that exhibits approximately the same patterns, but conceals sensitive data.
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    Funded Activity

    Discovery Projects - Grant ID: DP1096499

    Funder
    Australian Research Council
    Funding Amount
    $310,000.00
    Summary
    A more intelligent knowledge-based system apprentice. Our previous techniques already had an impact on Australian industry, with five Australian companies marketing such technology, and for three of these it is a central technology. We expect an early uptake of the enhancements we propose by these companies, greatly increasing their international competitiveness against other rule technologies. Three of these companies are very recent, so we would expect other company uptake of the new enhance .... A more intelligent knowledge-based system apprentice. Our previous techniques already had an impact on Australian industry, with five Australian companies marketing such technology, and for three of these it is a central technology. We expect an early uptake of the enhancements we propose by these companies, greatly increasing their international competitiveness against other rule technologies. Three of these companies are very recent, so we would expect other company uptake of the new enhanced technology. In turn Australian companies using the technology will improve their competitiveness in an increasingly knowledge-based economy by being able to more rapidly and easily deploy knowledge-based systems. Our previous techniques have already had a significant impact in medical practice.
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    Funded Activity

    Linkage Projects - Grant ID: LP0991428

    Funder
    Australian Research Council
    Funding Amount
    $255,000.00
    Summary
    Emergency Control of Catastrophic Disturbances in a Power System. Following the tragic events of 11 September 2001, there are increased concerns about the security and robustness of power systems to evolving spectra of threats, such as natural disasters (e.g., earthquakes and hurricanes), equipment failure, human error, or deliberate sabotage and attack by terrorists. In this project, pattern recognition of local parameter changes in distributed monitoring systems will be used to identify any th .... Emergency Control of Catastrophic Disturbances in a Power System. Following the tragic events of 11 September 2001, there are increased concerns about the security and robustness of power systems to evolving spectra of threats, such as natural disasters (e.g., earthquakes and hurricanes), equipment failure, human error, or deliberate sabotage and attack by terrorists. In this project, pattern recognition of local parameter changes in distributed monitoring systems will be used to identify any threatened breakdown in the power system. Once identified, methods based on intelligent agents will be used to trigger the appropriate countermeasures to maintain the integrity of transmission grids.
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