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pro vyhledávání: '"Baxter, Jonathan"'
Autor:
Baxter, Jonathan
Following [Diggle 2011, Greenland 1995], we give a simple formula for the Bayesian posterior density of a prevalence parameter based on unreliable testing of a population. This problem is of particular importance when the false positive test rate is
Externí odkaz:
http://arxiv.org/abs/2009.05446
Autor:
Baxter, Jonathan
Publikováno v:
in Learning to Learn (edited by Sebastian Thrun and Lorien Pratt), 159-179 (1998)
A Machine can only learn if it is biased in some way. Typically the bias is supplied by hand, for example through the choice of an appropriate set of features. However, if the learning machine is embedded within an {\em environment} of related tasks,
Externí odkaz:
http://arxiv.org/abs/2002.12364
Autor:
DeClerk, Leonie, Wells, Cheryl, Chasteen, Steven, Baxter, Jonathan, Martinez, Jessica, Rojo, Martha
Publikováno v:
In The Journal for Nurse Practitioners November-December 2023 19(10)
Autor:
Aberdeen, Douglas, Baxter, Jonathan
Publikováno v:
Euro-Par '00 Proceedings from the 6th International Euro-Par Conference on Parallel Processing (2000) Pages 980-983
Generalised matrix-matrix multiplication forms the kernel of many mathematical algorithms. A faster matrix-matrix multiply immediately benefits these algorithms. In this paper we implement efficient matrix multiplication for large matrices using the
Externí odkaz:
http://arxiv.org/abs/1912.04379
Autor:
Bartlett, Peter L., Baxter, Jonathan
In this paper, we derive a new model of synaptic plasticity, based on recent algorithms for reinforcement learning (in which an agent attempts to learn appropriate actions to maximize its long-term average reward). We show that these direct reinforce
Externí odkaz:
http://arxiv.org/abs/1911.07247
Autor:
Baxter, Jonathan
A form of generalisation error known as Off Training Set (OTS) error was recently introduced in [Wolpert, 1996b], along with a theorem showing that small training set error does not guarantee small OTS error, unless assumptions are made about the tar
Externí odkaz:
http://arxiv.org/abs/1912.05915
Autor:
Baxter, Jonathan
Publikováno v:
COLT 96 Proceedings of the ninth annual conference on Computational learning theory (1996) Pages 77-88
In this paper the problem of learning appropriate bias for an environment of related tasks is examined from a Bayesian perspective. The environment of related tasks is shown to be naturally modelled by the concept of an {\em objective} prior distribu
Externí odkaz:
http://arxiv.org/abs/1911.06129
Autor:
Baxter, Jonathan
Publikováno v:
In: Thrun S., Pratt L. (eds) Learning to Learn (1998). Pages 159-177
To measure the quality of a set of vector quantization points a means of measuring the distance between a random point and its quantization is required. Common metrics such as the {\em Hamming} and {\em Euclidean} metrics, while mathematically simple
Externí odkaz:
http://arxiv.org/abs/1911.06319
Autor:
Baxter, Jonathan
Publikováno v:
Advances in Neural Information Processing Systems 8, 1995, 169-175
In this paper the problem of {\em learning} appropriate domain-specific bias is addressed. It is shown that this can be achieved by learning many related tasks from the same domain, and a theorem is given bounding the number tasks that must be learnt
Externí odkaz:
http://arxiv.org/abs/1911.06164
Autor:
Baxter, Jonathan
Publikováno v:
COLT '95 Proceedings of the eighth annual conference on Computational learning theory (1995) 311-320
Probably the most important problem in machine learning is the preliminary biasing of a learner's hypothesis space so that it is small enough to ensure good generalisation from reasonable training sets, yet large enough that it contains a good soluti
Externí odkaz:
http://arxiv.org/abs/1911.05781