This warning means that the iterative routine used by LIBSVM to solve quadratic optimization problem in order to find the maximum margin hyperplane (i.e., parameters $w$ and $b$) separating your data ...

A general comment: if you want to make two classes of points linearly separable by mapping them into another space, that does not mean that your new space should necessarily have more dimensions than ...

I would suggest the following theoretical guidance. When you are using Gaussian RBF kernel, your separating surface will be based on a combination of bell-shaped surfaces centered at each support ...

For SVM that minimizes objective function $$\frac{1}{2}||w||^2 + C_1 \sum_{\xi_i: y_i=-1}^{l}\xi_i + C_2 \sum_{\xi_i: y_i=1}^{l}\xi_i$$ you can choose constants $C_1$ and $C_2$ inversely proportional ...

Assume that Gaussian RBF kernel $k(x, y)$ is defined on domain $X \times X$ where $X$ contains an infinite number of vectors. One can prove (Gaussian Kernels, Why are they full rank?) that for any set ...

Have you normalized the data? If not, and you have features measured on very different scales, then your zero coefficients in the equation of the SVM hyperplane may not be zeros, but merely some small ...

You can use a sigmoid function $f(d) = \frac{1}{1 + e^{-\alpha(d-\beta)}}$ to convert your SVM decision value $d = (w, x) + b$ into a number between 0 and 1 which can be treated as probability. You ...

So you don't want to add any other variables in your model (average age, level of education, ethnicity, etc.)? It may be useful to visualize these two variables on a geographical map. You may see ...

If your question is whether it is possible to separate without errors a linearly separable set of points by using polynomial kernel $k(x, z) = (\langle x, z \rangle + 1)^d$, $d > 1$, then the ...

For data point $x$ your SVM calculates decision value $d$ in the following way: d <- sum(w * x) + b If $d > 0$ then label of $x$ is $+1$, else it's $-1$. You can also get labels or decision ...

You can have a look at this "Mind Reading" game and at the details of its implementation. I think it is very relevant to your second question.

"anomaly detection" -- This is usually called detection of outliers. You can find many references via googling. "can more aggressions predict more self injurious behaviors" -- You can try one of the ...

Regarding decision trees, I would suggest the following. Assume that you have 10 training examples from class $C_1$ and 90 training examples from class $C_2$. You can use an ensemble of $N$ decision ...

Note that the image of $\phi$ is the unit circle $C$ centered at zero. So for any $y \in C$ $\phi^{-1}(y) = \{ay|a \in R, a \neq 0\}$, while for any $y \notin C$ $\phi^{-1}(y) = \emptyset$. Let's ...

I don't have any references on this either, but maybe the following thoughts will be helpful. First, why would you need all $w_i$ to be non-negative? If you need a black box that classifies your data, ...