A classifier is linear if its decision boundary on the feature space is a linear function: positive and negative examples are separated by an hyperplane.

This is what a SVM does by definition without the use of the kernel trick.

Also logistic regression uses linear decision boundaries. Imagine you trained a logistic regression and obtained the coefficients $\beta_{i}$, You might want to classify a test record $\mathbf{x}=\left(x_{1}, \ldots, x_{k}\right)$ if $P(\mathbf{x})>0.5$ Where the probability is obtained with your logistic regression by: $$P(\mathbf{x})=\frac{1}{1+e^{-\left(\beta_{0}+\beta_{1} x_{1}+\cdots+\beta_{k} x_{k}\right)}}$$

If you work out the math you see that P(x)>0.5 defines a hyperplane on the feature space which separates positive from negative examples.

With kNN you don’t have an hyperplane in general. Imagine some dense region of positive points. The decision boundary to classify test instances around those points will look like a curve – not a hyperplane.