The values in meas(xdata) represent some kind of measured data. SVM does not know or care how the measurements were taken or what they actually are. As far as SVM is concerned, it does not matter whether some parts are (say) temperature and other parts are (say) x-ray pixel intensity, and other parts are (say) acceleration along a particular axis.
"I mean why a set (x,y) is classified as BENIGN for example."
It just is. SVM does not care about cause and effect; at most it cares about correlation. If, for example, one of the readings were I2Sb2F11 concentration and another of the readings were H2S03, and high readings of I2Sb2F11 occurred in the samples marked class Tumor Grade 1, then SVM does not care whether I2Sb2F11 causes Tumor, or Tumor causes I2Sb2F11, or whether the H2S03 was reacting with the probe leads and leading to contamination that the healthy cells rejected but the Tumor cells were not able to flush.
The data you input for SVM does not in itself have an explanatory power: you just take a bunch of measurements of things that might be relevant, somehow, and you have some kind of external judgement about the class of the training samples, and SVM tries to figure out what the important correlations are. Once correlations are identified, then the researchers can go back and write up another grant to try to model causation.
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