Bias and Variance definitions Is it correct to say that, in the Bias-Variance trade-off:

the bias error represents the ability of the model to possibly map the trend of the training set ? 

and the variance represents how much the model is prone to variations if the dataset changes ?
I would say that both the definitions you gave above refer to variance. 

Variance is an index of model complexity. Thus:

high variance -> high complexity -> many degrees of freedom in model -> it overfits training data -> if dataset changes, accuracy degrades a lot

Bias is an index of the systematic error of the model, as it shows the difference between the expected value of various model's predictions for a given target output and the ground truth.