Tagged Questions

0
votes
0answers
52 views

Normalizing SVM predications to [0,1]

I have trained an linear SVM which takes a pair of objects, computes features and is expected to learn a semantic similarity function between objects(we can say that it predicts whether the two ...
2
votes
0answers
53 views

What are the most popular domain adaptation methods (for transfer learning)?

I understand supervised and unsupervised learning well, and would be able to identify some 'basic' examples of, for example, supervised classifcation as: SVMs Random Forests Logistic Regression ...
4
votes
2answers
190 views

How can I transform time series data so I can use simpler techniques for fault prediction?

I know this is primarily a statistics site, so if I am off-topic, please redirect me. I have a system with pumps that sometimes break and need to be replaced. I would like to be able to predict the ...
1
vote
1answer
227 views

Is the true relation between independent and dependent variables assumed to be a function or a distribution?

In classification and regression tasks, we try to learn from a training data set a function mapping a independent variable $X$ to a dependent variable $Y$. When evaluating the error rate of a ...
6
votes
1answer
89 views

Correlation between two nodes of a single layer MLP for joint-Gaussian input

Let's say you have a jointly gaussian vector random variable $\mathbf{x}$, with mean $\mathbf{M}$ and covariance $\mathbf{S}$. I now transform each scalar element of $\mathbf{x}$ , say $x_j$, with a ...
10
votes
5answers
1k views

SVD dimensionality reduction for time series of different length

I am using Singular Value Decomposition as a dimensionality reduction technique. Given N vectors of dimension D, the idea is to ...