Bijdragen van 10.91.149.183

7 jun 2006

• 10:487 jun 2006 10:48 wijz gesch −2 Artificial Neural Networks →* What is the learning constant, and which values should you give it ? Explain its role.(05-06)
• 10:477 jun 2006 10:47 wijz gesch +1 Artificial Neural Networks →* What is the learning constant, and which values should you give it ? Explain its role.(05-06)
• 10:467 jun 2006 10:46 wijz gesch +1.561 Artificial Neural Networks →* What is the learning constant, and which values should you give it ? Explain its role.(05-06)

25 mei 2006

• 11:1125 mei 2006 11:11 wijz gesch +119 Artificial Neural Networks →Lecture 10
• 08:5725 mei 2006 08:57 wijz gesch +136 Artificial Neural Networks →* What is the relation between the Bayesian and maximum likelihood approach?

24 mei 2006

• 20:3624 mei 2006 20:36 wijz gesch 0 Artificial Neural Networks →Discuss the relevance of input representation and the number of hidden units in a network construction.
• 20:3524 mei 2006 20:35 wijz gesch 0 Artificial Neural Networks →Discuss the relevance of input representation and the number of hidden units in a network construction.
• 20:3524 mei 2006 20:35 wijz gesch 0 Artificial Neural Networks →Discuss the relevance of input representation and the number of hidden units in a network construction.
• 20:3524 mei 2006 20:35 wijz gesch +2 Artificial Neural Networks →Discuss the relevance of input representation and the number of hidden units in a network construction.
• 20:3124 mei 2006 20:31 wijz gesch +50 Artificial Neural Networks →Discuss the relevance of input representation and the number of hidden units in a network construction.
• 20:2724 mei 2006 20:27 wijz gesch +148 Artificial Neural Networks →* Give the connection between Sanger's rule and principal component analysis.
• 20:2324 mei 2006 20:23 wijz gesch +50 Artificial Neural Networks →* Explain Oja's rule in the framework of unsupervised Hebbian learning.
• 19:1524 mei 2006 19:15 wijz gesch +34 Artificial Neural Networks →What is an RBF (radial basis function) network? How does it work?
• 19:1024 mei 2006 19:10 wijz gesch +54 Artificial Neural Networks →What is an RBF (radial basis function) network? How does it work?
• 19:1024 mei 2006 19:10 wijz gesch +1.932 Artificial Neural Networks →* What is an RBF (radial basis function) network? How does it work?
• 19:1024 mei 2006 19:10 wijz gesch +80 Artificial Neural Networks →Lecture 5
• 18:4124 mei 2006 18:41 wijz gesch +47 Artificial Neural Networks →* Explain the adaptive resonance theory algorithm.
• 18:1324 mei 2006 18:13 wijz gesch −2 Artificial Neural Networks →* Explain the standard competitive learning rule.
• 18:1324 mei 2006 18:13 wijz gesch +52 Artificial Neural Networks →* Explain the standard competitive learning rule.
• 17:4224 mei 2006 17:42 wijz gesch −20 Artificial Neural Networks →Explain learning with a critic. Can it be applied for the control of a plant?
• 17:4224 mei 2006 17:42 wijz gesch +697 Artificial Neural Networks →Explain learning with a critic. Can it be applied for the control of a plant?
• 17:2624 mei 2006 17:26 wijz gesch +80 Artificial Neural Networks →* Explain the associative reward-penalty algorithm.
• 13:2024 mei 2006 13:20 wijz gesch +603 Artificial Neural Networks →* Explain the energy function of the travelling salesman problem.
• 11:1424 mei 2006 11:14 wijz gesch −4 Artificial Neural Networks →* Explain the discrete and continuous perceptron and delta learning rule. What is the difference and why?
• 11:1324 mei 2006 11:13 wijz gesch +2 Artificial Neural Networks →* Explain the discrete and continuous perceptron and delta learning rule. What is the difference and why?
• 11:1224 mei 2006 11:12 wijz gesch +548 Artificial Neural Networks →* Explain the discrete and continuous perceptron and delta learning rule. What is the difference and why?
• 10:4924 mei 2006 10:49 wijz gesch +2.017 Artificial Neural Networks →* Explain the backpropagation algorithm and the choice of the parameters and early stopping rule.
• 10:4224 mei 2006 10:42 wijz gesch +276 Artificial Neural Networks →* Explain the discrete and continuous perceptron and delta learning rule. What is the difference and why?