Active Learning for High Throughput Screening
Kurt De Grave,Jan Ramon,Luc De Raedt +2 more
- 13 Oct 2008
- Vol. 5255, pp 185-196
28
TL;DR: An algorithm based on Gaussian processes for tackling active k-optimization is developed and evaluated on a challenging set of tasks related to structure-activity relationship prediction.
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Abstract: An important task in many scientific and engineering disciplines is to set up experiments with the goal of finding the best instances (substances, compositions, designs) as evaluated on an unknown target function using limited resources. We study this problem using machine learning principles, and introduce the novel task of active k-optimization. The problem consists of approximating the kbest instances with regard to an unknown function and the learner is active, that is, it can present a limited number of instances to an oracle for obtaining the target value. We also develop an algorithm based on Gaussian processes for tackling active k-optimization, and evaluate it on a challenging set of tasks related to structure-activity relationship prediction.
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References
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
30.8K
•Book
Pattern Recognition and Machine Learning
Christopher M. Bishop
- 17 Aug 2006
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
•Book
Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop
- 01 Aug 2006
TL;DR: Looking for competent reading resources?
10.1K
Gaussian Processes For Machine Learning
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- 01 Jan 2016
TL;DR: The gaussian processes for machine learning is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can get it instantly.
10K
Efficient Global Optimization of Expensive Black-Box Functions
TL;DR: This paper introduces the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering and shows how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule.