The train tag has no wiki summary.
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60 views
Forming training set for Multinomial Naive Bayes
Is it true that Multinomial Naive Bayes requires equally by count training data for each class to get best performance?
For example, we forming classifier for three classes - Japan, China, Korea.
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2answers
138 views
Combining labeled and unlabeled data for training
When training my algorithm, if I can get some i.e. data my future test data that has no labels can it improve my algorithm's efficiency, is there any mathematical proof for it?
PS: I think ...
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203 views
Logistic Regression Cost Function issue in Matlab
I'm trying to implement a logistic regression function in matlab. I calculated the theta values, linear regression cost function is converging and then I use those parameters in logistic regression ...
2
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1answer
109 views
SVM retrain on whole dataset for final model --> overfitting?
i am training a SVM (RBF kernel) with a dataset of ~1500 samples (balanced) using fminsearch on the CV error for parameter optimization (C and s).
After i found the "best" parameters (local optima ...
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79 views
What is Generalization errror on training set. How can I see it on weka?
I get output like this ..
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1answer
150 views
Can a neural network output represent a posterior probability?
I seem to remember from years ago when I first read Bishop's ANN book that it is possible to construct a neural network such that the outputs should represent the posterior probability that I would ...
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5answers
406 views
Is using the same data for feature selection and cross-validation biased or not?
We have a small dataset (about 250 samples * 100 features) on which we want to build a binary classifier after selecting the best feature subset. Lets say that we partition the data into:
Training, ...
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1answer
113 views
Machine Learning and training time: is it really relevant?
I have a question regarding the time needed for training a classifier. I am facing the specific problem of Sentiment Analysis (classification of text as pos/neg/neu).
(Excepting online learning ...
