  In conventional prediction tasks, a machine learning algorithm outputs a
single best model that globally optimizes its objective function, which
typically is accuracy. Therefore, users cannot access the other models
explicitly. In contrast to this, multiple model enumeration attracts increasing
interests in non-standard machine learning applications where other criteria,
e.g., interpretability or fairness, than accuracy are main concern and a user
may want to access more than one non-optimal, but suitable models. In this
paper, we propose a K-best model enumeration algorithm for Support Vector
Machines (SVM) that given a dataset S and an integer K>0, enumerates the K-best
models on S with distinct support vectors in the descending order of the
objective function values in the dual SVM problem. Based on analysis of the
lattice structure of support vectors, our algorithm efficiently finds the next
best model with small latency. This is useful in supporting users's interactive
examination of their requirements on enumerated models. By experiments on real
datasets, we evaluated the efficiency and usefulness of our algorithm.
