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Een gebruiker met 95 bewerkingen. Account aangemaakt op 13 apr 2008.
11 dec 2008
- 23:0711 dec 2008 23:07 wijz gesch +3.748 N Initiatie tot Ondernemen New page: Lijst met Examenvragen "Initiatie tot Ondernemen" Prof. Dr. Ir. Koenraad Debackere K.U. Leuven Academiejaar 2001-2002 Hierna volgt een lijst met 15 vragen waaruit zal worden geput voor h...
10 dec 2008
- 15:1110 dec 2008 15:11 wijz gesch 0 VSIP/formularium →Eiffel
- 15:1110 dec 2008 15:11 wijz gesch +30 VSIP/formularium →Eiffel
14 jun 2008
- 18:1814 jun 2008 18:18 wijz gesch +3 Computergrafieken →2008-06-14
- 18:1814 jun 2008 18:18 wijz gesch 0 Computergrafieken →2008-06-14
- 18:1814 jun 2008 18:18 wijz gesch +436 Computergrafieken →Examens
13 apr 2008
- 16:1113 apr 2008 16:11 wijz gesch +8 Artificial Neural Networks →What is a CNN template? (05-06)
- 16:1013 apr 2008 16:10 wijz gesch +7 Artificial Neural Networks →What are typical application areas of cellular neural networks?(05-06)
- 16:1013 apr 2008 16:10 wijz gesch +8 Artificial Neural Networks →* What is a cellular neural network and how does it work ? What are its main advantages? (05-06)
- 16:0213 apr 2008 16:02 wijz gesch +8 Artificial Neural Networks →* Explain the Adaline network and the LMS learning rule. (05-06)
- 15:5913 apr 2008 15:59 wijz gesch +8 Artificial Neural Networks →Explain the problem of echo cancellation and the use of adaline networks to overcome it. (05-06)
- 15:5813 apr 2008 15:58 wijz gesch +8 Artificial Neural Networks →* What are the motivations for fuzzifying the perceptron rule? How can one do this? (05-06)
- 15:5813 apr 2008 15:58 wijz gesch +8 Artificial Neural Networks →* What are the basic ideas implemented by support vector machines? Explain.(05-06)
- 15:5613 apr 2008 15:56 wijz gesch +8 Artificial Neural Networks →Explain: Bayesian learning treats the issue of model complexity differently than cross-validation does. (05-06)
- 15:5513 apr 2008 15:55 wijz gesch +8 Artificial Neural Networks →* Explain how a network learns in the Bayesian approach.(05-06)
- 15:5513 apr 2008 15:55 wijz gesch +8 Artificial Neural Networks →What is the relation between the Bayesian and maximum likelihood approach? (05-06)
- 15:5113 apr 2008 15:51 wijz gesch +8 Artificial Neural Networks →Explain the tiling algorithm.(05-06)
- 15:5113 apr 2008 15:51 wijz gesch +8 Artificial Neural Networks →What is the aim of pruning and construction algorithms? Give an example. (05-06)
- 15:5013 apr 2008 15:50 wijz gesch +8 Artificial Neural Networks →* Give the connection between Sanger's rule and principal component analysis.(05-06)
- 15:5013 apr 2008 15:50 wijz gesch +7 Artificial Neural Networks →Explain Oja's rule in the framework of unsupervised Hebbian learning.(05-06)
- 15:4713 apr 2008 15:47 wijz gesch +7 Artificial Neural Networks →What is an RBF (radial basis function) network? How does it work? (05-06)
- 15:4313 apr 2008 15:43 wijz gesch +7 Artificial Neural Networks →* What is a self-organizing map? Give an example.(05-06)
- 15:4213 apr 2008 15:42 wijz gesch +7 Artificial Neural Networks →Explain the adaptive resonance theory algorithm.(05-06)
- 15:2913 apr 2008 15:29 wijz gesch +8 Artificial Neural Networks →* Explain vector quantization and learning vector quantization. What is the difference?(05-06)
- 15:2913 apr 2008 15:29 wijz gesch +8 Artificial Neural Networks →Explain the standard competitive learning rule.(05-06)
- 15:2613 apr 2008 15:26 wijz gesch +7 Artificial Neural Networks →* Explain learning with a critic. (Can it be applied for the control of a plant?) When can it be applied?(05-06)
- 15:2613 apr 2008 15:26 wijz gesch +7 Artificial Neural Networks →Explain the associative reward-penalty algorithm. (05-06)
- 15:2113 apr 2008 15:21 wijz gesch +5 Artificial Neural Networks →Het Exploratie-Exploitatie probleem:
- 15:2013 apr 2008 15:20 wijz gesch +131 Artificial Neural Networks →* Explain the recurrent back-propagation algorithm.
- 15:1713 apr 2008 15:17 wijz gesch +8 k Artificial Neural Networks →* Describe the design of a neural network for the weighted matching problem.(05-06)
- 15:1613 apr 2008 15:16 wijz gesch +8 Artificial Neural Networks →Explain the energy function of the travelling salesman problem.(05-06)
- 15:1513 apr 2008 15:15 wijz gesch +8 Artificial Neural Networks →How can one solve optimization problems using the Hopfield model? Give an example.(05-06)
- 15:0813 apr 2008 15:08 wijz gesch +53 Artificial Neural Networks →Lecture 3
- 15:0713 apr 2008 15:07 wijz gesch +8 k Artificial Neural Networks →What is an attractor neural network and how does it work? Give an example.(05-06)
- 15:0513 apr 2008 15:05 wijz gesch +7 Artificial Neural Networks →Explain that certain classification problems can be solved with two layer Neural Networks, that cannot be solved with one layer Neural Networks.(05-06)
- 15:0413 apr 2008 15:04 wijz gesch +123 Artificial Neural Networks →Lecture 2
- 15:0213 apr 2008 15:02 wijz gesch +7 Artificial Neural Networks →* Explain discriminant functions in classifier systems and their relation to the perceptron. (05-06)
- 15:0113 apr 2008 15:01 wijz gesch +99 Artificial Neural Networks →Lecture 2
- 14:5813 apr 2008 14:58 wijz gesch +6 Artificial Neural Networks →What is the learning constant, and which values should you give it ? Explain its role.(05-06)
- 14:4013 apr 2008 14:40 wijz gesch −1 Artificial Neural Networks →Which kind of activation functions can one use for neural networks? What are advantages and disadvantages with respect to learning?(05-06)(07-08))
- 14:4013 apr 2008 14:40 wijz gesch +8 Artificial Neural Networks →Which kind of activation functions can one use for neural networks? What are advantages and disadvantages with respect to learning?(05-06)
- 14:3913 apr 2008 14:39 wijz gesch +7 Artificial Neural Networks →* Explain the early stopping rule and overtraining.(05-06)
- 14:3713 apr 2008 14:37 wijz gesch +7 Artificial Neural Networks →In which areas is it useful to apply neural networks and where is it not useful? (05-06)
- 14:3313 apr 2008 14:33 wijz gesch +7 Artificial Neural Networks →Explain the difference between learning, memorization and generalization.(05-06)
- 14:3113 apr 2008 14:31 wijz gesch +7 Artificial Neural Networks →Lecture 1