I don't have much knowledge about it but there is a way to use a sparse autoencoder in Weka? At this time, I've just used MPLAutoencoder and don't have certain if I can configure it for sparsing too. Thank you.
I've asked the author of MPLAutoencoder and there isn't an implementation of a sparse autoencoder in Weka yet.
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I am running an lstm model. My data has a predictor variable which is normally distributed.
I was just wondering using LSTM on such distribution is a good idea?
Thanks.
Normally distributed is pretty much symmetrical about its mean. For that reason, using Gaussian Naive Bayes would be better to choose.
Is this process correct?
Suppose We have a bunch of data such as MNIST.
We just feed all these data(without label) to RBM and resample each data from trained model.
Then output can be treated as new data for classification.
Do I understand it correctly?
What is the purpose of using RBM?
You are correct, RBMs are a form of unsupervised learning algorithm that are commonly used to reduce the dimensionality of your feature space. Another common approach is to use autoencoders.
RBMs are trained using the contrastive divergence algorithm. The best overview of this algorithm comes from Geoffrey Hinton who came up with it.
https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf
A great paper about how unsupervised learning improves performance can be found at http://jmlr.org/papers/volume11/erhan10a/erhan10a.pdf. The paper shows that unsupervised learning provides better generalization and filters (if using CRBMs)
I am trying to implement Multiclass classification in WEKA.
I have lot of rows, say bank transactions, and one is tagged as Food,Medicine,Rent,etc. I want to develop a classifier which can be trained with the previous data I have and predict the class it can belong to for future transactions. If I am right this is Multiclass and not multilabel since each transaction can belong to only one class.
Below are a few algorithms I am considering
Naive Bayes
Multinomial Logistic Regression
Multiclass SVM
Max Entropy
Neural Networks (if possible)
In my data Number of features <<< Number of transactions and hence I am thinking of one vs rest binary classifier instead of one vs one.
Are there any other algorithms I should lok into which will help with my goal?
Is there any algos that I put are useless for my goal?
Also,I found that scikit-learn in Python is better than WEKA but I can run scikit-learn only on one processor. Is this true?
Answers to any question would be helpful.
Thanks!
You can look at RandomForest which is a well known classifier and quite efficient.
In scikit-learn, you have some class that can be used over several core like RandomForestClassifier. It has a constructor parameter that can be used to define the number of core or a value that will use every available core. Look at the documentation, constructor that contains n_jobs parameter can be used over several core
WEKA has profound support for kNN classifiers (many different distances and etc.)
Unfortunately WEKA doesn't support multi-label problems.
One of the solutions can be to use binary relevance approach.
I am not sure whether it's a correct workaround? What do you think?
You can try Meka which is based on Weka and is expected to handle multilabel classification problems.
I need to train the sample image set to classification of them. But i have bit knowledge about the SVM technique to do the coding stuff. please help me to do the programming part of that.
The OpenCV documentation of the SVM provides a small example on how to use it: link