[cygnids]

The PLA algorithm, eqn. 1.3, can be used to partition linearly separable data. What I'm curious is to what optimization criteria underlies eqn. 1.3? The figures on pp. 6-7 show that for a 2D case we have the algorithm converge to some straight line decision boundary, and it is also qualitatively clear that many different straight-lines, would "work" equally well (ie give the same E_{in} error rate); however PLA converges to a specific solution. The PLA algorithm seems to provide both, an optimization criteria, and a method for solution too. The opt. criteria gives provides uniqueness. Can the optimization criteria underlying PLA (eqn 1.3) be spelled out explicitly? Thank you.