# How does the support vector machine constraint imply that sample selection bias will not systematically affect the output of the optimisation?

I am currently studying the paper Learning and Evaluating Classifiers under Sample Selection Bias by Bianca Zadrozny. In section 3.4. Support vector machines, the author says the following:

3.4. Support vector machines
In its basic form, the support vector machine (SVM) algorithm (Joachims, 2000a) learns the parameters $$a$$ and $$b$$ describing a linear decision rule $$h(x) = \text{sign}(a \cdot x + b),$$ whose sign determines the label of an example, so that the smallest distance between each training example and the decision boundary, i.e. the margin, is maximized. Given a sample of examples $$(x_i, y_i)$$, where $$y_i \in \{ -1, 1 \}$$, it accomplishes margin maximization by solving the following optimization problem: $$\text{minimize:} \ V(a, b) = \dfrac{1}{2} a \cdot a \\ \text{subject to:} \ \forall i : \ y_i[a \cdot x_i + b] \ge 1$$ The constraint requires that all examples in the training set are classified correctly. Thus, sample selection bias will not systematically affect the output of this optimization, assuming that the selection probability $$P(s = 1 \mid x)$$ is greater than zero for all $$x$$.

How does the constraint that all examples in the training set are classified correctly imply that sample selection bias will not systematically affect the output of the optimisation? Furthermore, why is it necessary to assume that the selection probability is greater than zero for all $$x$$? These are not clear to me.