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Research Topic : NEURAL NETWORK
Field of Research : Pattern Recognition
Australian State/Territory : NSW
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  • Funded Activity

    Discovery Projects - Grant ID: DP0773584

    Funder
    Australian Research Council
    Funding Amount
    $210,000.00
    Summary
    Automatic detection of the circle of Willis in neuro-images using multi-scale gradient calculation and knowledge-based genetic algorithms. Stroke is the third most common cause of death and a major contributor to long term disability in Australia. The most efficient way of preventing stroke from happening is to detect related symptoms early. The group of cerebral blood vessels that closely related to strokes is the circle of Willis (CoW). We build a system that can automatically detect and quan .... Automatic detection of the circle of Willis in neuro-images using multi-scale gradient calculation and knowledge-based genetic algorithms. Stroke is the third most common cause of death and a major contributor to long term disability in Australia. The most efficient way of preventing stroke from happening is to detect related symptoms early. The group of cerebral blood vessels that closely related to strokes is the circle of Willis (CoW). We build a system that can automatically detect and quantify CoW in neuroimages, providing ways of preventing strokes from happening. The project will enhance Australia¡¯s leading position in promoting and maintaining good health, especially in preventive healthcare.
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    Funded Activity

    Discovery Projects - Grant ID: DP0343346

    Funder
    Australian Research Council
    Funding Amount
    $159,264.00
    Summary
    Analysing Iterative Machine Learning Algorithms with Information Geometric Methods. Online machine learning problems arise from situations where data is provided a point at a time. There are many classical algorithms for solving such problems based on the principle of stochastic gradient descent. Recent research by the CIs and others have thrown up interesting but diverse geometric connections that offer new insights. The proposed research aims to integrate the understanding of these algori .... Analysing Iterative Machine Learning Algorithms with Information Geometric Methods. Online machine learning problems arise from situations where data is provided a point at a time. There are many classical algorithms for solving such problems based on the principle of stochastic gradient descent. Recent research by the CIs and others have thrown up interesting but diverse geometric connections that offer new insights. The proposed research aims to integrate the understanding of these algorithms with the aim of designing algorithms better able to exploit prior knowledge, and to extend existing algorithms to new problem domains thus offering well principled and well understood algorithms for solving a variety of novel online problems.
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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: DP0560207

    Funder
    Australian Research Council
    Funding Amount
    $232,000.00
    Summary
    Teaching Software Agents to Play. This project is fundamental to computational intelligence and human brain science. Both strongly impact on the community and the nation. They form the backbone of contemporary information science and are key to our National Research Priorities. This research is of great community benefit. Our results will enable everyone to rapidly search, analyse and interpret vast amounts of information that is irretrievable by current methods. They will also enliven interacti .... Teaching Software Agents to Play. This project is fundamental to computational intelligence and human brain science. Both strongly impact on the community and the nation. They form the backbone of contemporary information science and are key to our National Research Priorities. This research is of great community benefit. Our results will enable everyone to rapidly search, analyse and interpret vast amounts of information that is irretrievable by current methods. They will also enliven interactive entertainment by spontaneously creating unique, tailor-made music. In sum, we are making a leap forward in the crucial area of collaborative intelligence.
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    Funded Activity

    Discovery Projects - Grant ID: DP0881829

    Funder
    Australian Research Council
    Funding Amount
    $225,000.00
    Summary
    Evolving largest scale concept structures. This project will find new methods of collective intelligence- many small computer programs acting in synergy. Such software has many applications from data mining to networks of sensors, but the main focus will be on one of the Grand Challenges of artificial intelligence -- the Japanese game of Go. Go is at least as difficult as Chess but computers are far from reaching the skill of human experts. Insights into the human brain from autism and savants w .... Evolving largest scale concept structures. This project will find new methods of collective intelligence- many small computer programs acting in synergy. Such software has many applications from data mining to networks of sensors, but the main focus will be on one of the Grand Challenges of artificial intelligence -- the Japanese game of Go. Go is at least as difficult as Chess but computers are far from reaching the skill of human experts. Insights into the human brain from autism and savants will form the foundations of the new computational approaches we will develop.
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