Radial basis function network configuration using genetic algorithms

Billings, S.A.; Zheng, Guang

doi:10.1016/0893-6080(95)00029-y

Cited by 219 publications

(73 citation statements)

References 11 publications

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“…Several interesting hybrid algorithms have been proposed that use various flavors of genetic algorithms, usually to determine the number, size, and location of the basis functions [1,3,6,9,11,12,13,22,27,37]. Most of these algorithms have not been proposed for use on reinforcement learning problems, where there is no labeled training data.…”

Section: Approachmentioning

confidence: 99%

Evolving neural networks for fractured domains

Kohl

Miikkulainen

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

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Evolution of neural networks, or neuroevolution, bas been successful on many low-level control problems such as pole balancing, vehicle control, and collision warning. However, high-level strategy problems that require the integration of multiple sub-behaviors have remained difficult for neuroevolution to solve. This paper proposes the hypothesis that such problems are difficult because they are fractured: the correct action varies discontinuously as the agent moves from state to state. This hypothesis is evaluated on several examples of fractured high-level reinforcement learning domains. Standard neuroevolution methods such as NEAT indeed have difficulty solving them. However, a modification of NEAT that uses radial basis function (RBF) nodes to make precise local mutations to network output is able to do much better. These results provide a better understanding of the different types of reinforcement learning problems and the limitations of current neuroevolution methods. Thus, they lay the groundwork for creating the next generation of neuroevolution algorithms that can learn strategic high-level behavior in fractured domains.

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Section: Approachmentioning

confidence: 99%

Evolving neural networks for fractured domains

Kohl

Miikkulainen

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

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“…This algorithm uses function approximation (curve fitting) to construct the network. The RBF network's activation of hidden units is based on the distance between the input vector and a prototype vector [27][28][29]. In recent years, it has become a very popular algorithm due to its simple structure and high training efficiency.…”

Section: Radial Basis Function (Rbf) Networkmentioning

confidence: 99%

An Appraisal Model Based on a Synthetic Feature Selection Approach for Students’ Academic Achievement

Cheng

Liu

2017

Symmetry

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Obtaining necessary information (and even extracting hidden messages) from existing big data, and then transforming them into knowledge, is an important skill. Data mining technology has received increased attention in various fields in recent years because it can be used to find historical patterns and employ machine learning to aid in decision-making. When we find unexpected rules or patterns from the data, they are likely to be of high value. This paper proposes a synthetic feature selection approach (SFSA), which is combined with a support vector machine (SVM) to extract patterns and find the key features that influence students' academic achievement. For verifying the proposed model, two databases, namely, "Student Profile" and "Tutorship Record", were collected from an elementary school in Taiwan, and were concatenated into an integrated dataset based on students' names as a research dataset. The results indicate the following: (1) the accuracy of the proposed feature selection approach is better than that of the Minimum-Redundancy-Maximum-Relevance (mRMR) approach; (2) the proposed model is better than the listing methods when the six least influential features have been deleted; and (3) the proposed model can enhance the accuracy and facilitate the interpretation of the pattern from a hybrid-type dataset of students' academic achievement.

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“…Given this error measurement, we then train our radial-basis function network using a modified version of the genetic algorithm as proposed in [3]. The only difference in our modified training process is that we derive the error of the network through the above error measurement process.…”

Section: B Determining the Error Of The Prediction Networkmentioning

confidence: 99%

Learning to rank videos personally using multiple clues

Jiang

Lau

2009

Proceedings of the ACM International Conference on Image and Video Retrieval

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In this paper, we introduce a new learning based video content similarity model. The model leverages on multiple clues on the contents of a video and can be used to rank videos in a personalized way. The key to produce a personalized video ranking is to have a good estimate of pairwise video content similarity, which is realized through meta-learning using a radial-basis function network. Four aspects of a video are considered in deriving the video content similarity in our method. The training data to our model are acquired in the form of user judged preference relationships regarding video content similarities. With the optimized video content similarity estimation obtained by our algorithm, we can produce a personalized video ranking that matches more closely an individual user's watching interest over a collection of videos. The video ranking results generated by our prototype system are compared with the groundtruth rankings supplied by the individual users as well as rankings by the commercial video website YouTube. The results confirm the advantages of our method in generating personalized video rankings.

show abstract

Radial basis function network configuration using genetic algorithms

Cited by 219 publications

References 11 publications

Evolving neural networks for fractured domains

Evolving neural networks for fractured domains

An Appraisal Model Based on a Synthetic Feature Selection Approach for Students’ Academic Achievement

Learning to rank videos personally using multiple clues

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