We focus on the problem of prediction with confidence and describe a recently developed learning algorithm called transductive confidence machine for making qualified region predictions. Its main advantage, in compari...
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We focus on the problem of prediction with confidence and describe a recently developed learning algorithm called transductive confidence machine for making qualified region predictions. Its main advantage, in comparison with other classifiers, is that it is well-calibrated, with number of prediction errors strictly controlled by a given predefined confidence level. We apply the transductive confidence machine to the problems of acute leukaemia and ovarian cancer prediction using microarray and proteomics pattern diagnostics, respectively. We demonstrate that the algorithm performs well, yielding well-calibrated and informative predictions whilst maintaining a high level of accuracy.
In the task of classification, most learning methods are suitable only for certain data types. For the hybrid dataset consists of nominal and numeric attributes, to apply the learning algorithms, some attributes must ...
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In this paper we present a machine learning system that can accurately predict the transitions between frames in a video sequence. We propose a set of novel features and describe how to use dominant features based on ...
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ISBN:
(纸本)3540228810
In this paper we present a machine learning system that can accurately predict the transitions between frames in a video sequence. We propose a set of novel features and describe how to use dominant features based on a coarse-to-fine strategy to accurately predict video transitions.
A common objective in image analysis is dimensionality reduction. the most often used data-exploratory technique withthis objective is principal component analysis. We propose a new method based on the projection of ...
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ISBN:
(纸本)3540228810
A common objective in image analysis is dimensionality reduction. the most often used data-exploratory technique withthis objective is principal component analysis. We propose a new method based on the projection of the images as matrices after a Procrustes rotation and show that it leads to a better reconstruction of images.
AURA (Advanced Uncertain Reasoning Architecture) is a parallel pattern matching technology intended for high-speed approximate search and match operations on large unstructured datasets. this paper represents how the ...
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ISBN:
(纸本)3540228810
AURA (Advanced Uncertain Reasoning Architecture) is a parallel pattern matching technology intended for high-speed approximate search and match operations on large unstructured datasets. this paper represents how the AURA technology is extended and used to search the engine data within a major UK eScience Grid project (DAME) for maintenance of Rolls-Royce aero-engines and how it may be applied in other areas. Examples of its use will be presented.
In order for a neural network ensemble to generalise properly, two factors are considered vital. One is the diversity and the other is the accuracy of the networks that comprise the ensemble. there exists a tradeoff a...
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ISBN:
(纸本)3540228810
In order for a neural network ensemble to generalise properly, two factors are considered vital. One is the diversity and the other is the accuracy of the networks that comprise the ensemble. there exists a tradeoff as to what should be the optimal measures of diversity and accuracy. the aim of this paper is to address this issue. We propose the DIVACE algorithm which tries to produce an ensemble as it searches for the optimum point on the diversity-accuracy curve. the DIVACE algorithm formulates the ensemble learning problem as a multi-objective problem explicitly.
In this paper, we focus on the problem of prediction with confidence and describe the recently developed transductive confidence machines (TCM). TCM allows us to make predictions within predefined confidence levels, t...
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ISBN:
(纸本)3540228810
In this paper, we focus on the problem of prediction with confidence and describe the recently developed transductive confidence machines (TCM). TCM allows us to make predictions within predefined confidence levels, thus providing a controlled and calibrated classification environment. We apply the TCM to the problem of proteomics pattern diagnostics. We demonstrate that the TCM performs well, yielding accurate, well-calibrated and informative predictions in both online and offline learning settings.
Under the Bayesian Ying-Yang (BYY) harmony learningtheory, a harmony function has been developed for Gaussian mixture model with an important feature that, via its maximization through a gradient learning rule, model...
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ISBN:
(纸本)3540228810
Under the Bayesian Ying-Yang (BYY) harmony learningtheory, a harmony function has been developed for Gaussian mixture model with an important feature that, via its maximization through a gradient learning rule, model selection can be made automatically during parameter learning on a set of sample data from a Gaussian mixture. this paper proposes two further gradient learning rules, called conjugate and natural gradient learning rules, respectively, to efficiently implement the maximization of the harmony function on Gaussian mixture. It is demonstrated by simulation experiments that these two new gradient learning rules not only work well, but also converge more quickly than the general gradient ones.
Finite mixture models are commonly used in pattern recognition. Parameters of these models are usually estimated via the Expectation Maximization algorithm. this algorithm is modified earlier to handle incomplete data...
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ISBN:
(纸本)3540228810
Finite mixture models are commonly used in pattern recognition. Parameters of these models are usually estimated via the Expectation Maximization algorithm. this algorithm is modified earlier to handle incomplete data. However, the modified algorithm is sensitive to the occurrence of outliers in the data and to the overlap among data classes in the data space. Meanwhile, it requires the number of missing values to be small in order to produce good estimations of the model parameters. therefore, a new algorithm is proposed in this paper to overcome these problems. A comparison study shows the preference of the proposed algorithm to other algorithms commonly used in the literature including the modified Expectation Maximization algorithm.
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