We address the problem of incorporating transformation invariance in kernels for pattern analysis with kernel methods. We introduce a new class of kernels by so called Haar-integration over transformations. This results in kernel functions, which are positive definite, have adjustable invariance, can capture simultaneously various continuous or discrete transformations and are applicable in various kernel methods. We demonstrate these properties on toy examples and experimentally investigate the real-world applicability on an image recognition task with support vector machines. For certain transformations remarkable complexity reduction is demonstrated. The kernels hereby achieve state-of-the-art results, while omitting drawbacks of existing methods.