Breast cancer is one of the most common neoplasms in women and it is a leading cause of worldwide death. However, it is also among the most curable cancer types if it can be diagnosed early through a proper mammographic screening procedure. So, suitable computer aided detection systems can help the radiologists to detect many subtle signs, normally missed during the first visual examination. This study proposes a Gabor filtering method for the extraction of textural features by multi-sized evaluation windows applied to the four probabilistic distribution moments. Then, an adaptive strategy for data selection is used to eliminate the most irrelevant pixels. Finally, a pixel-based classification step is applied by using Support Vector Machines in order to identify the tumor pixels. During this part we also estimate the appropriate kernel parameters to obtain an accurate configuration for the four existing kernels. Experiments have been conducted on different training-test partitions of mini-MIAS database, which is commonly used among researchers who apply machine learning methods for breast cancer diagnosis. The improved performance of our framework is evaluated using several measures: classification accuracy, positive and negative predictive values, receiver operating characteristic curves and confusion matrix.