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We introduce a novel method for the transcription of polyphonic piano music by discriminative training of support vector machines (SVMs). As features, we use pitch activations computed by supervised non-negative matrix factorization from low-level spectral features. Different approaches to low-level feature extraction, NMF dictionary learning and activation feature extraction are analyzed in a large-scale evaluation on eight hours of piano music including synthesized and real recordings. We conclude that the proposed method delivers state-of-the-art results and clearly outperforms SVMs using simple spectral features.