1,722,102 research outputs found
Saunders, C W, WX8918
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/415407Surname: SAUNDERS. Given Name(s) or Initials: C W. Military Service Number or Last Known Location: WX8918. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 43445.235981
Item: [2016.0049.47668] "Saunders, C W, WX8918
Efficient Implementation and Experimental Testing of Transductive Algorithms for Predicting with Confidence
No full textSupport Vector Machines (SVM's) and other kernel based methods have grown in popularity in recent years. Although they have many benefits, such as the ability to deal with a large number of parameters, one drawback of these successful techniques is their lack of the ability to provide rigorous confidence measures for the predictions they make. This thesis is devoted to the efficient implementation and experimental testing of transductive algorithms developed at the computer science department, Royal Holloway. The algorithms are tested against several benchmark data sets, and methods for comparing quantitative confidence values are described and evaluated. These techniques and other machine-learning methods are also applied to the industrial application of fault diagnosis and automated repair. An extensive case study of applying these machine learning techniques to a real-world problem is carried out. Many problems such as data collection and representation -- which are common to most real-world applications of machine learning techniques, but sometimes over-sighted in literature -- are highlighted and discussed
Saunders, C S (Clarence Samuel), WX7211
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/415406Surname: SAUNDERS. Given Name(s) or Initials: C S (CLARENCE SAMUEL). Military Service Number or Last Known Location: WX7211. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 35645.235979
Item: [2016.0049.47667] "Saunders, C S (Clarence Samuel), WX7211
Application of Support Vector Machines to Fault Diagnosis and Automated Repair
No full textIn this paper we consider the benefits of applying modern machine learning techniques to the problem of Fault Diagnosis and Automated Repair. In the modern manufacturing environment, many aspects of the production line are logged automatically by various systems. These records are put to a multitude of uses including assisting stock control, and monitoring and improving the manufacturing process. This approach has lead to the accumulation of a huge amount of high-dimensional data, and does require new methods to handle it. In this paper we ask if the information commonly held by many companies can be used to assist the repair of faulty products on the production line. We examine the possibility of using pattern recognition techniques to determine correct repairs for faults from past production history. The relative merits of this method compared to other approaches (such as model-based reasoning) are also discussed. Finally, we give some preliminary results which indicate that pattern recognition methods such as the highly acclaimed Support Vector machine can be successfully applied in this area
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