I do not understand what are the différence between neural network and SVM.
In both cases there are learning/training mecanism...
Thanks
Check out this thread, which includes a good discussion of the topic and links to others.
Related
I want to know if Autoencoders are computationally lighter than other neural networks such as MLP with the same number of neurons. I have read in some papers that Autoencoders train the network faster, and I could check it in python, the training part is faster than a MLP. However, all these papers mention it briefly. I would like to know if there are some papers that explain it in more detail, because I could not find them.
Thank you in advance.
EncogModel is extremely useful thanks to its use of VersatileMLDataSet and the ability to perform cross validation.
However I can't see a way to create an Elman, Jordan or other RNN. Is this possible using EncogModel?
EncogModel does not support recurrent neural networks. It would be a decent change to make it do this, because recurrent neural networks are time-series, the EncogModel class would need to be extended to support sequences.
I have a different sets of vectors for an object. These vectors are different and are extracted from a particular shape. I want to train my Neural Network in matlab to recognize this particular shape. So that when I input another different vectors of similarity of that particular object, the neural network is able to differentiate and output either '1' or '0'
I am new to this neural network stuffs and I hope that someone could give me some valuable pointers.
First of all have a look to this pdf explaining the Neural Network Toolbox.
Here you can download a tutorial on pattern recognition with neural networks with matlab.
I hope this helps on your task.
To understand machine learning concepts in general and neural networks in particular, this resource will be usefull www.ml-class.org
I am a student working on an internship project where in we are using Bayesian networks to predict a possible outcome from a given set of discrete parent variables.We now intend to use artificial neural network to do the task.So could any one please help me out with the similarities and differences between Bayesian networks and artificial neural network?Any suggestions as how to proceed with migration would be helpful.
Thanks
Similarity
Both use directed graphs.
Difference
In Bayesian networks the vertices and edges have meaning- The network structure itself gives you valuable information about conditional dependence between the variables. With Neural Networks the network structure does not tell you anything.
I'm looking for computationally heavy tasks to implement with CUDA and wonder if neural networks or bayesian networks might apply. This is not my question, though, but rather what the relation between the two network types is. They seem very related, especially if you look at bayesian networks with a learning capability (which the article on wikipedia mentions). At a glance, bayesian networks look at bit like a specific type of neural networks. Can anyone sum up their relationship, and if there is any connection beyond the apparent similarity?
Bayesian networks represent independence (and dependence) relationships between variables. Thus, the links represent conditional relationships in the probabilistic sense. Neural networks, generally speaking, have no such direct interpretation, and in fact the intermediate nodes of most neural networks are discovered features, instead of having any predicate associated with them in their own right.
It is reported that Bayesian networks are more resistant to the "overtraining" that is seen in some neural networks. In other words some neural networks become so "trained" to the observed measurements used in training that they aren't useful for the general cases.
Indeed they are. I see a bayesian network as a neural network applying the Baye's Theorem on large scale, but I don't remember details. I know where you can find them and I recommend this book for that.