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 Gavin Brown

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Average citations per article6.71
Citation Count282
Publication count42
Publication years2003-2017
Available for download14
Average downloads per article261.71
Downloads (cumulative)3,664
Downloads (12 Months)365
Downloads (6 Weeks)30
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41 results found Export Results: bibtexendnoteacmrefcsv

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1
September 2016 Machine Learning: Volume 104 Issue 2-3, September 2016
Publisher: Kluwer Academic Publishers
Bibliometrics:
Citation Count: 1

We provide a unifying perspective for two decades of work on cost-sensitive Boosting algorithms. When analyzing the literature 1997---2016, we find 15 distinct cost-sensitive variants of the original algorithm; each of these has its own motivation and claims to superiority--so who should we believe? In this work we critique the ...
Keywords: Cost-sensitive, Boosting, Class imbalance, Classifier calibration

2 published by ACM
December 2015 ACM Transactions on Programming Languages and Systems (TOPLAS): Volume 38 Issue 2, January 2016
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 6,   Downloads (12 Months): 121,   Downloads (Overall): 533

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Current parallelizing compilers can tackle applications exercising regular access patterns on arrays or affine indices, where data dependencies can be expressed in a linear form. Unfortunately, there are cases that independence between statements of code cannot be guaranteed and thus the compiler conservatively produces sequential code. Programs that involve extensive ...
Keywords: multicore processors, runtime parallelization, Thread-level speculation, automatic parallelization, speculative parallelization

3
October 2015 BIG DATA '15: Proceedings of the 2015 IEEE International Conference on Big Data (Big Data)
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 0

With the growth of high dimensional data, feature selection is a vital component of machine learning as well as an important stand alone data analytics tool. Without it, the computation cost of big data analytics can become unmanageable and spurious correlations and noise can reduce the accuracy of any results. ...

4
October 2015 Pattern Recognition Letters: Volume 64 Issue C, October 2015
Publisher: Elsevier Science Inc.
Bibliometrics:
Citation Count: 0

We study a dichotomy of scientific styles, unifying and diversifying, as proposed by Freeman J. Dyson. We discuss the extent to which the dichotomy transfers from the natural sciences (where Dyson proposed it) to the field of Pattern Recognition. To address this we must firstly ask what it means to ...
Keywords: Diversifying, Unifying, Nature of pattern recognition, Dyson

5
October 2015 Proceedings of the 14th International Conference on The Semantic Web - ISWC 2015 - Volume 9366
Publisher: Springer-Verlag New York, Inc.
Bibliometrics:
Citation Count: 1

Automated acquisition, or learning, of ontologies has attracted research attention because it can help ontology engineers build ontologies and give domain experts new insights into their data. However, existing approaches to ontology learning are considerably limited, e.g. focus on learning descriptions for given classes, require intense supervision and human involvement, ...

6
October 2015 Revised Selected Papers of the 12th International Experiences and Directions Workshop on Ontology Engineering - Volume 9557
Publisher: Springer-Verlag New York, Inc.
Bibliometrics:
Citation Count: 0

An ontology is a machine-processable representation of knowledge about a domain of interest.

7
September 2015 ECMLPKDD'15: Proceedings of the 2015th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Publisher: Springer
Bibliometrics:
Citation Count: 0

The importance of Markov blanket discovery algorithms is twofold: as the main building block in constraint-based structure learning of Bayesian network algorithms and as a technique to derive the optimal set of features in filter feature selection approaches. Equally, learning from partially labelled data is a crucial and demanding area ...
Keywords: markov blanket discovery, semi supervised, mutual information, partially labelled, positive unlabelled

8
July 2015 ICML'15: Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37
Publisher: JMLR.org
Bibliometrics:
Citation Count: 3

Learning in adversarial settings is becoming an important task for application domains where attackers may inject malicious data into the training set to subvert normal operation of data-driven technologies. Feature selection has been widely used in machine learning for security applications to improve generalization and computational efficiency, although it is ...

9
March 2015 Information Sciences: an International Journal: Volume 296 Issue C, March 2015
Publisher: Elsevier Science Inc.
Bibliometrics:
Citation Count: 0

Data with multi-valued categorical attributes can cause major problems for decision trees. The high branching factor can lead to data fragmentation, where decisions have little or no statistical support. In this paper, we propose a new ensemble method, Random Ordinality Ensembles (ROE), that reduces this problem, and provides significantly improved ...
Keywords: Binary split, Classifier ensemble, Categorical data, Decision tree, Multi-way split

10
September 2014 ECMLPKDD'14: Proceedings of the 2014th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part III
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 0

We propose a set of novel methodologies which enable valid statistical hypothesis testing when we have only positive and unlabelled (PU) examples. This type of problem, a special case of semi-supervised data, is common in text mining, bioinformatics, and computer vision. Focusing on a generalised likelihood ratio test, we have ...

11
August 2014 S+SSPR 2014: Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition - Volume 8621
Publisher: Springer-Verlag New York, Inc.
Bibliometrics:
Citation Count: 0

In this paper we present a framework to unify information theoretic feature selection criteria for multi-label data. Our framework combines two different ideas; expressing multi-label decomposition methods as composite likelihoods and then showing how feature selection criteria can be derived by maximizing these likelihood expressions. Many existing criteria, until now ...

12
July 2014 KR'14: Proceedings of the Fourteenth International Conference on Principles of Knowledge Representation and Reasoning
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

We propose a novel approach for performance prediction of OWL reasoners that selects suitable, small ontology subsets, and then extrapolates reasoner's performance on them to the whole ontology. We investigate intercorrelation of ontology features using PCA and discuss various error measures for performance prediction.

13
May 2014 IEEE Transactions on Knowledge and Data Engineering: Volume 26 Issue 5, May 2014
Publisher: IEEE Educational Activities Department
Bibliometrics:
Citation Count: 0

In this paper, we present a novel ensemble method random projection random discretization ensembles (RPRDE) to create ensembles of linear multivariate decision trees by using a univariate decision tree algorithm. The present method combines the better computational complexity of a univariate decision tree algorithm with the better representational power of ...

14
April 2013 The Journal of Machine Learning Research: Volume 14 Issue 1, January 2013
Publisher: JMLR.org
Bibliometrics:
Citation Count: 11
Downloads (6 Weeks): 1,   Downloads (12 Months): 10,   Downloads (Overall): 88

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Fano's inequality lower bounds the probability of transmission error through a communication channel. Applied to classification problems, it provides a lower bound on the Bayes error rate and motivates the widely used Infomax principle. In modern machine learning, we are often interested in more than just the error rate. In ...
Keywords: cost-sensitive risk, balanced error rate, F-score (Fβ-measure), conditional entropy, lower/upper bound

15 published by ACM
January 2013 ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers: Volume 9 Issue 4, January 2013
Publisher: ACM
Bibliometrics:
Citation Count: 8
Downloads (6 Weeks): 3,   Downloads (12 Months): 37,   Downloads (Overall): 427

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Thread-Level Speculation (TLS) overcomes limitations intrinsic with conservative compile-time auto-parallelizing tools by extracting parallel threads optimistically and only ensuring absence of data dependence violations at runtime. A significant barrier for adopting TLS (implemented in software) is the overheads associated with maintaining speculative state. Based on previous TLS limit studies, we ...
Keywords: multicore processors, inspector threads, loop-level parallelism, runtime parallelization, Thread-level speculation, memory overhead, SPECjvm2008, speculative parallelization

16
January 2012 The Journal of Machine Learning Research: Volume 13, 3/1/2012
Publisher: JMLR.org
Bibliometrics:
Citation Count: 83
Downloads (6 Weeks): 8,   Downloads (12 Months): 57,   Downloads (Overall): 745

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We present a unifying framework for information theoretic feature selection, bringing almost two decades of research on heuristic filter criteria under a single theoretical interpretation. This is in response to the question: "what are the implicit statistical assumptions of feature selection criteria based on mutual information?". To answer this, we ...
Keywords: conditional likelihood, mutual information, feature selection
Also published in:
January 2012  The Journal of Machine Learning Research: Volume 13 Issue 1, January 2012

17
July 2011 UAI'11: Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence
Publisher: AUAI Press
Bibliometrics:
Citation Count: 0

In this paper, we derive a novel probabilistic model of boosting as a Product of Experts. We re-derive the boosting algorithm as a greedy incremental model selection procedure which ensures that addition of new experts to the ensemble does not decrease the likelihood of the data. These learning rules lead ...

18 published by ACM
July 2011 GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 0,   Downloads (12 Months): 10,   Downloads (Overall): 65

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In recent years there have been efforts to develop a probabilistic framework to explain the workings of a Learning Classifier System. This direction of research has met with limited success due to the intractability of complicated heuristic training rules used by the learning classifier systems. In this paper, we derive ...
Keywords: learning classifier system, mixture of experts, probabilistic modeling, ucs

19 published by ACM
July 2011 GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 0,   Downloads (12 Months): 9,   Downloads (Overall): 50

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UCS is a Learning Classifier System (LCS) which evolves condition-action rules for supervised classification tasks. In UCS the fitness of a rule is based on its accuracy raised to a power ν, and this fitness is used in both the search for good rules (via a genetic algorithm) and in ...
Keywords: fitness evaluation, learning classifier systems, voting margin

20 published by ACM
June 2011 ISMM '11: Proceedings of the international symposium on Memory management
Publisher: ACM
Bibliometrics:
Citation Count: 6
Downloads (6 Weeks): 5,   Downloads (12 Months): 34,   Downloads (Overall): 398

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MapReduce has been widely accepted as a simple programming pattern that can form the basis for efficient, large-scale, distributed data processing. The success of the MapReduce pattern has led to a variety of implementations for different computational scenarios. In this paper we present MRJ , a MapReduce Java framework for ...
Keywords: garbage collection, java, mapreduce, machine learning
Also published in:
November 2011  ACM SIGPLAN Notices - ISMM '11: Volume 46 Issue 11, November 2011



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