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Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.

1
vote
3answers
I'm doing some machine learning and get a set of optimum weights in the end. I'd like to verify that these weights are by and large the same no matter how many times I train on the data. I assume that …
asked May 10 '13 by siamii
5
votes
2answers
I sometimes see that the cost function, along with the regularizer is divided by 1/2m where m is the number of examples. When we are trying to find the minimum of the cost, why does scaling by this am …
asked Apr 29 '13 by siamii
2
votes
2answers
I have a trading software that buys and sells loans. There's an auction site where borrowers ask for some money and lenders bid on them until the borrower is fully funded and the auction ends. There's …
asked Aug 16 '13 by siamii
0
votes
1answer
I'm doing machine learning with a training set, validation set and test set. I train with the L_BFGS algorithm. The training converges all the time. I have the default accuracy from scipy, which is q …
asked Apr 17 '13 by siamii
3
votes
1answer
I'm trying to model some time series data, and I've been reading about tapped delay line and sliding window to transform the input data. In my understanding a sliding window with windows size 1 shift …
asked Apr 12 '12 by siamii
3
votes
1answer
I'm want to do a PhD in NLP and I'm defining the topic at the moment. I've heard that NLP can be used to track online sentiment which in turn can be used for algorithmic trading on stock markets. See …
asked Jan 11 '12 by siamii
12
votes
2answers
For example, one wants to predict house prices and have two input features the length and width of the house. Sometimes, one also includes 'derived' polynomial input features, such as area, which is l …
asked Mar 18 '12 by siamii
14
votes
7answers
Alternatively, to predict foreign exchange markets. I know this can get pretty complicated, so as an introduction, I'm looking for a simple prediction algorithm that has some accuracy. (It's for a M. …
asked Jan 20 '12 by siamii
0
votes
Okay, so I came to this conclusion. Can anyone verify it please? Online gradient descent: $$ \frac{\partial}{\partial w_{j,k}}J =\frac{\partial}{\partial w_{j,k}}\frac{1}{2}||XW-Y||_F^2+\lambda\su …
answered Feb 16 '13 by siamii
4
votes
2answers
Background: This is the costfunction of Mean Regularized Multi Task Learning. This is a typical linear regression learning model, with the only difference being that there's multiple instances of trai …
asked Feb 15 '13 by siamii
5
votes
0answers
I want to fit a Gaussian Process with about 50,000 training examples and 130 features using Scikit-learn. Right now, I only have 1 theta hyperparameters as I run the process with all defaults. But I g …
asked May 22 '13 by siamii
33
votes
5answers
I have a streaming data set, examples are available one at a time. I would need to do multi class classification on them. As soon as I fed a training example to the learning process, I have to discard …
asked Apr 7 '12 by siamii
24
votes
5answers
I have a data set with a set of features. Some of them are binary $(1=$ active or fired, $0=$ inactive or dormant), and the rest are real valued, e.g. $4564.342$. I want to feed this data to a machin …
asked May 18 '13 by siamii
14
votes
3answers
Let's say I have a logistic regression classifier. In normal batch learning, I'd have a regularizer term to prevent overfitting and keep my weights small. I'd also normalize and scale my features. In …
asked Jul 13 '13 by siamii