Results 1  10
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1,911
Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization
, 1993
"... The paper describes a rankbased fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to a ..."
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Cited by 633 (15 self)
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to allow direct intervention of an external decision maker (DM). Finally, the MOGA is generalised further: the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM. It is the interaction between the two that leads to the determination of a
Variational algorithms for approximate Bayesian inference
, 2003
"... The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coherent way, avoids overfitting problems, and provides a principled basis for selecting between alternative models. Unfortunately the computations required are usually intractable. This thesis presents ..."
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Cited by 440 (9 self)
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the theoretical core of the thesis, generalising the expectationmaximisation (EM) algorithm for learning maximum likelihood parameters to the VB EM algorithm which integrates over model parameters. The algorithm is then specialised to the large family of conjugateexponential (CE) graphical models, and several
OnLine QLearning Using Connectionist Systems
, 1994
"... Reinforcement learning algorithms are a powerful machine learning technique. However, much of the work on these algorithms has been developed with regard to discrete finitestate Markovian problems, which is too restrictive for many realworld environments. Therefore, it is desirable to extend these ..."
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Cited by 381 (1 self)
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Reinforcement learning algorithms are a powerful machine learning technique. However, much of the work on these algorithms has been developed with regard to discrete finitestate Markovian problems, which is too restrictive for many realworld environments. Therefore, it is desirable to extend
Generalised prior subspace analysis for polyphonic pitch transcription
 in Proc. Int. Conf. on Digital Audio Effects (DAFx
, 2005
"... A reformulation of Prior Subspace Analysis (PSA) is presented, which restates the problem as that of fitting an undercomplete signal dictionary to a spectrogram. Further, a generalization of PSA is derived which allows the transcription of polyphonic pitched instruments. This involves the translatio ..."
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Cited by 10 (3 self)
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the translation of a single frequency prior subspace of a note to approximate other notes, overcoming the problem of needing a separate basis function for each note played by an instrument. Examples are then demonstrated which show the utility of the generalised PSA algorithm for the purposes of polyphonic pitch
Support Vector Machines for Classification and Regression
 UNIVERSITY OF SOUTHAMPTON, TECHNICAL REPORT
, 1998
"... The problem of empirical data modelling is germane to many engineering applications.
In empirical data modelling a process of induction is used to build up a model of the
system, from which it is hoped to deduce responses of the system that have yet to be observed.
Ultimately the quantity and qualit ..."
Abstract

Cited by 357 (5 self)
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. Consequently
the problem is nearly always ill posed (Poggio et al., 1985) in the sense of Hadamard
(Hadamard, 1923). Traditional neural network approaches have suffered difficulties with
generalisation, producing models that can overfit the data. This is a consequence of the
optimisation algorithms used
The Power of Amnesia: Learning Probabilistic Automata with Variable Memory Length
 Machine Learning
, 1996
"... . We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic finite automata which we name Probabilistic Suffix Automata (PSA). Though hardness results are known for learning distributions gene ..."
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Cited by 226 (17 self)
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. We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic finite automata which we name Probabilistic Suffix Automata (PSA). Though hardness results are known for learning distributions
Gaussian groups and Garside groups, two generalisations of Artin groups
 PROC. LONDON MATH. SOC
, 1999
"... It is known that a number of algebraic properties of the braid groups extend to arbitrary finite Coxeter type Artin groups. Here we show how to extend the results to more general groups that we call Garside groups. Define a Gaussian monoid to be a finitely generated cancellative monoid where the ex ..."
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Cited by 137 (25 self)
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Gaussian monoid and of a Garside monoid. Braid groups and, more generally, finite Coxeter type Artin groups are Garside groups. We determine algorithmic criterions in terms of presentations for recognizing Gaussian and Garside monoids and groups, and exhibit infinite families of such groups. We describe
On the Generalisation of Soft Margin Algorithms
 IEEE Transactions on Information Theory
, 2000
"... Generalisation bounds depending on the margin of a classier are a relatively recent development. They provide an explanation of the performance of stateoftheart learning systems such as Support Vector Machines (SVM) [12] and Adaboost [24]. The diculty with these bounds has been either their lack ..."
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Cited by 13 (6 self)
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Generalisation bounds depending on the margin of a classier are a relatively recent development. They provide an explanation of the performance of stateoftheart learning systems such as Support Vector Machines (SVM) [12] and Adaboost [24]. The diculty with these bounds has been either their lack
NonStandard Reasoning Services for the Debugging of Description Logic Terminologies
, 2003
"... Current Description Logic reasoning systems provide only limited support for debugging logically erroneous knowledge bases. In this paper we propose new nonstandard reasoning services which we designed and implemented to pinpoint logical contradictions when developing the medical terminology DICE. ..."
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Cited by 168 (9 self)
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. We provide complete algorithms for unfoldable ACCTBoxes based on minimisation of axioms using Boolean methods for minimal unsatisfiabilitypresening subTBoxes, and an incomplete bottomup method for generalised incoherencepreserving terminologies. 1
Associative hierarchical CRFs for object class image segmentation
 in Proc. ICCV
, 2009
"... Most methods for object class segmentation are formulated as a labelling problem over a single choice of quantisation of an image space pixels, segments or group of segments. It is well known that each quantisation has its fair share of pros and cons; and the existence of a common optimal quantisat ..."
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Cited by 172 (25 self)
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powerful graph cut based move making algorithms. Our framework generalises much of the previous work based on pixels or segments. We evaluate its efficiency on some of the most challenging datasets for object class segmentation, and show it obtains stateoftheart results. 1.
Results 1  10
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1,911