Tagged Questions
1
vote
0answers
37 views
Which Regression methods are suitable for binary valued features and continuous output?
I want to build a machine learning model to regression on continuous output given binary valued features(0,1). the dimension of my problem is around 200. which of the flowing methods seems suitable ...
0
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0answers
43 views
Energy estimation through machine learning
Greedings to everybody.
I have the dataset which you can find here, containing many different characteristics of different houses, including their types of heating, or the number of adults and ...
0
votes
1answer
17 views
in nonlinear binary classification problems, which is the optimal dimension for make it lineary separable?
My question pertains to linear separability with hyperplanes in a support vector machine.
Is posible to determinate the optimal dimension in which i have to transform a training data set for make it ...
1
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0answers
47 views
How to choose kernel functions for support vector regression
Are there any good resources regarding how to design kernels for regression problems, specifically time-series regression type of problem. I am finding the choice of a kernel for regression extremely ...
1
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0answers
65 views
Regression using SVMs
I have set of time series data tuples: {(1,22), (2, 25), (3, 18), (4, 26), ... so on}
I want to estimate Y(t+1) using support vector machine regression technique. I have found numerous publications ...
0
votes
1answer
136 views
Identifiying performance of individual feature components in accuracy of support vector methods
I'm using support vector regression (not classification) for a problem and it's working well.
However, in the older method that former lab members developed (a basic linear model, with weights ...
4
votes
1answer
82 views
SVM regression with longitudinal data
I have about 500 variables per patient, each variable has one continous value and is measured at three different time points (after 2 month and after 1 year). With the regression I would like to ...
3
votes
1answer
104 views
Prediction using machine learning
Say I have some data for past 5 years and I have trained my classifier (anything decision tree, svm etc.) based on that i.e. given the appropriate input feature data and correct output labeling.
Now ...
0
votes
1answer
144 views
How to avoid multicolinearity in SVM input data?
Do you know of any techniques that allows one to avoid and get rid of multicolinearity in SVM input data? We all know that if multicolinearity exists, explanatory variables have a high degree of ...
3
votes
1answer
306 views
Are support vector regression and kernel ridge regression used for the same type of problems?
Does, in some sense, Kernel Ridge Regression refer to the same class of problems as Support Vector Regression? Should I use them almost equivalently?
3
votes
1answer
129 views
Is there an equivalent measure of explanatory power in Support Vector Regression (SVR) like in Least Squares?
In Least Squares regression, we have statistics like $R^2$ to measure the strength of the explanatory variables. Are there similar measures when we are dealing with Support Vector Regression (SVR)? ...
2
votes
1answer
327 views
Strange kernlab's relevance vector machine predictions
I am using a relevance vector machine as implemented in the kernlab-package in R, trained on a dataset with 360 continuous variables (features) and 60 examples (also continuous, so it's a relevance ...
0
votes
1answer
130 views
Differences and connections among different machine learning methods
For the following popular data mining methods:
SVM, neural network, logistic regression, random forest, classification tree, Naïve Bayes classifier, regression;
How to compare them in terms of ...
9
votes
1answer
306 views
Support vector machines and regression
There's already been an excellent discussion on how support vector machines handle classification, but I'm very confused about how support vector machines generalize to regression.
Anyone care to ...
3
votes
2answers
285 views
Active learning using SVM Regression
I have trained an SVM Regression model using training data, $x_1,x_2,\dots,x_N$.
I want to perform active learning to improve the model; i.e., I want to add more samples to the training data and ...
6
votes
1answer
725 views
Understanding SVM regression: objective function and “flatness”
SVMs for classification make intuitive sense to me: I understand how minimizing $||\theta||^2$ yields the maximum margin. However, I don't understand that objective in the context of regression. ...
