Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
A graph-constructive approach to solving systems of geometric constraints
ACM Transactions on Graphics (TOG)
Sketch-based pruning of a solution space within a formal geometric constraint solver
Artificial Intelligence
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems
Statistics and Computing
User profiling with Case-Based Reasoning and Bayesian Networks
International Joint Conference, 7th Ibero-American Conference, 15th Brazilian Symposium on AI, IBERAMIA-SBIA 2000, Open Discussion Track Proceedings on AI
Deciding Parameter Values with Case-Based Reasoning
Proceedings of the First United Kingdom Workshop on Progress in Case-Based Reasoning
Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks
Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks
Gradient-Based Optimization of Hyperparameters
Neural Computation
A model for parameter setting based on Bayesian networks
Engineering Applications of Artificial Intelligence
Experience management: foundations, development methodology, and internet-based applications
Experience management: foundations, development methodology, and internet-based applications
Sequential update of Bayesian network structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning Bayesian networks: the combination of knowledge and statistical data
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Statistical analysis of the main parameters involved in the designof a genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
IEEE Transactions on Neural Networks
Combinations of case-based reasoning with other intelligent methods
International Journal of Hybrid Intelligent Systems - CIMA-08
Experimental evaluation of an automatic parameter setting system
Expert Systems with Applications: An International Journal
On-the-fly calibrating strategies for evolutionary algorithms
Information Sciences: an International Journal
Bayesian networks to predict data mining algorithm behavior in ubiquitous computing environments
MSM'10/MUSE'10 Proceedings of the 2010 international conference on Analysis of social media and ubiquitous data
Using automated individual white-list to protect web digital identities
Expert Systems with Applications: An International Journal
Optimising retrieval phase in CBR through Pearl and JLO algorithms for medical diagnosis
International Journal of Advanced Intelligence Paradigms
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The parameter setting of an algorithm that will result in optimal performance differs across problem instance domains. Users spend a lot of time tuning algorithms for their specific problem domain, and this time could be saved by an automatic approach for parameter tuning. In this paper, we present a system that recommends the parameter configuration of an algorithm that solves a problem, conditioned by the particular features of the current problem instance to be solved. The proposed system is based on a basic adjustment model designed by authors (Pavon, R., Diaz, F., & Luzon, V. (2008). A model for parameter setting based on Bayesian networks. Engineering Applications of Artificial Intelligence, 21(1), 14-25) in which starting from experimental results concerning the search for solutions to several instances of the problem, a Bayesian network (BN) is induced and tries to infer the best configuration for the algorithm used. However, taking into account that the optimal parameter configuration may differ considerably across problem instances of a specific domain, the present work extends the former incorporating additional information about problem instances and using the case-based reasoning (CBR) methodology as the framework integrator for the different instances from the same problem, where each problem instance deals with a specific BN. In this way, the system will automatically recommend a parameter configuration for a given algorithm according to the characteristics of the problem instance at hand and past experience of similar instances. As an example, we empirically evaluate our Bayesian CBR system to tune a genetic algorithm for solving the root identification problem. The experimental results demonstrate the validity of the model proposed.