I need simple matlab code for prediction
i want to use multilayer perceptron
I have 4 input and 1 output
I need code for training the algorithm and other one for test with new data
This is a previous answer from #jbrown:
Geoff Hinton is the man when it comes to multilayer perceptrons. His Science paper from 2006 used a special class of MLP called an "autoencoder" that was successful in digit recognition, facial recognition, and document classification (all of which have real world applications): Reducing the Dimensionality of Data with Neural Networks
Fortunately, they also published the Matlab code.
Also take a look here
Well....as you are asking for code directly. I don't think that there is someone that will give you the code,but I can give you the direction. If you have access to MATLAB(R), there is nice implementation of MLP and all you have to do is to fill in the blank...
But there is always better one, but I believe that the best one is always the one that you implemented yourself.
GOOD LUCK.
"MLP Neural Network with Backpropagation", that is what you are searching for:
http://www.mathworks.com/matlabcentral/fileexchange/54076-mlp-neural-network-with-backpropagation
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I'm in the overtures of designing a prose imitation system. It will read a bunch of prose, then mimic it. It's mostly for fun so the mimicking prose doesn't need to make too much sense, but I'd like to make it as good as I can, with a minimal amount of effort.
My first idea is to use my example prose to train a classifying feed-forward neural network, which classifies its input as either part of the training data or not part. Then I'd like to somehow invert the neural network, finding new random inputs that also get classified by the trained network as being part of the training data. The obvious and stupid way of doing this is to randomly generate word lists and only output the ones that get classified above a certain threshold, but I think there is a better way, using the network itself to limit the search to certain regions of the input space. For example, maybe you could start with a random vector and do gradient descent optimisation to find a local maximum around the random starting point. Is there a word for this kind of imitation process? What are some of the known methods?
How about Generative Adversarial Networks (GAN, Goodfellow 2014) and their more advanced siblings like Deep Convolutional Generative Adversarial Networks? There are plenty of proper research articles out there, and also more gentle introductions like this one on DCGAN and this on GAN. To quote the latter:
GANs are an interesting idea that were first introduced in 2014 by a
group of researchers at the University of Montreal lead by Ian
Goodfellow (now at OpenAI). The main idea behind a GAN is to have two
competing neural network models. One takes noise as input and
generates samples (and so is called the generator). The other model
(called the discriminator) receives samples from both the generator
and the training data, and has to be able to distinguish between the
two sources. These two networks play a continuous game, where the
generator is learning to produce more and more realistic samples, and
the discriminator is learning to get better and better at
distinguishing generated data from real data. These two networks are
trained simultaneously, and the hope is that the competition will
drive the generated samples to be indistinguishable from real data.
(DC)GAN should fit your task quite well.
My question is can we use CNN for feature extraction and then can we use this extracted feature as an input to another classification algorithm like SVM.
Thanks
Yes, this has already been done and well documented in several research papers, like CNN Features off-the-shelf: an Astounding Baseline for Recognition and How transferable are features in deep neural networks?. Both show that using CNN features trained on one dataset, but tested on a different one usually perform very well or beat the state of the art.
In general you can take the features from the layer before the last, normalize them and use them with another classifier.
Another related technique is fine tuning, where after training a network, the last layer is replaced and retrained, but previous layers' weights are kept fixed.
I have used the neural network software for predicting the continous data. Obviously the prediction was better than the results obtained through regression analysis. Now i would like to derive a model expression from the trained weights obtained from the training of the continous data through the software, as suggested by many researchers on how to interpret the trained weights and biases for deriving the model equation i tried to derive one from the similar lines.
After deriving the equation i found that the equation was not able to replicate the same results as given by the neural network software. so i am exploring the new methods to derive the equation. I want to know where i am going wrong and if any one can provide me steps for deriving one it will be helpful.
I have read sometime ago about what you're talking about, but with some diferences. It would probably be useful to you. It's called 'knowledge distilling', if I remember well, and it is a way of extracting the knowledge inside the blackbox that a neural network is. It consists, roughly speaking, in training a simpler model that is easier to interpret, but preserving al the predictive power of the original neural network. I'm speaking from memory, so I'm sorry about the lack of detail. A search on Google will provide the exact references for it.
Hope to have helped.
I have decided to use a feed-forward NN with back-propagation training for my OCR application for Handwritten text and the input layer is going to be with 32*32 (1024) neurones and at least 8-12 out put neurones.
I found Neuroph easy to use by reading some articles at the same time Encog is few times better in performance. Considering the parameters in my scenario which API is the most suitable one. And I appreciate if u can comment on the number of input nodes i have taken, is it too large value (Although it is out of the topic)
First my disclaimer, I am one of the main developers on the Encog project. This means I am more familiar with Encog that Neuroph and perhaps biased towards it. In my opinion, the relative strengths of each are as follows. Encog supports quite a few interchangeable machine learning methods and training methods. Neuroph is VERY focused on neural networks and you can express a connection between just about anything. So if you are going to create very custom/non-standard (research) neural networks of different typologies than the typical Elman/Jordan, NEAT, HyperNEAT, Feedforward type networks, then Neuroph will fit the bill nicely.
I'm trying to build an app to detect images which are advertisements from the webpages. Once I detect those I`ll not be allowing those to be displayed on the client side.
Basically I'm using Back-propagation algorithm to train the neural network using the dataset given here: http://archive.ics.uci.edu/ml/datasets/Internet+Advertisements.
But in that dataset no. of attributes are very high. In fact one of the mentors of the project told me that If you train the Neural Network with that many attributes, it'll take lots of time to get trained. So is there a way to optimize the input dataset? Or I just have to use that many attributes?
1558 is actually a modest number of features/attributes. The # of instances(3279) is also small. The problem is not on the dataset side, but on the training algorithm side.
ANN is slow in training, I'd suggest you to use a logistic regression or svm. Both of them are very fast to train. Especially, svm has a lot of fast algorithms.
In this dataset, you are actually analyzing text, but not image. I think a linear family classifier, i.e. logistic regression or svm, is better for your job.
If you are using for production and you cannot use open source code. Logistic regression is very easy to implement compared to a good ANN and SVM.
If you decide to use logistic regression or SVM, I can future recommend some articles or source code for you to refer.
If you're actually using a backpropagation network with 1558 input nodes and only 3279 samples, then the training time is the least of your problems: Even if you have a very small network with only one hidden layer containing 10 neurons, you have 1558*10 weights between the input layer and the hidden layer. How can you expect to get a good estimate for 15580 degrees of freedom from only 3279 samples? (And that simple calculation doesn't even take the "curse of dimensionality" into account)
You have to analyze your data to find out how to optimize it. Try to understand your input data: Which (tuples of) features are (jointly) statistically significant? (use standard statistical methods for this) Are some features redundant? (Principal component analysis is a good stating point for this.) Don't expect the artificial neural network to do that work for you.
Also: remeber Duda&Hart's famous "no-free-lunch-theorem": No classification algorithm works for every problem. And for any classification algorithm X, there is a problem where flipping a coin leads to better results than X. If you take this into account, deciding what algorithm to use before analyzing your data might not be a smart idea. You might well have picked the algorithm that actually performs worse than blind guessing on your specific problem! (By the way: Duda&Hart&Storks's book about pattern classification is a great starting point to learn about this, if you haven't read it yet.)
aplly a seperate ANN for each category of features
for example
457 inputs 1 output for url terms ( ANN1 )
495 inputs 1 output for origurl ( ANN2 )
...
then train all of them
use another main ANN to join results