  One of the most popular ML algorithms, AdaBoost, can be derived from the dual
of a relative entropy minimization problem subject to the fact that the
positive weights on the examples sum to one. Essentially, harder examples
receive higher probabilities. We generalize this setup to the recently
introduced {\it tempered exponential measure}s (TEMs) where normalization is
enforced on a specific power of the measure and not the measure itself. TEMs
are indexed by a parameter $t$ and generalize exponential families ($t=1$). Our
algorithm, $t$-AdaBoost, recovers AdaBoost~as a special case ($t=1$). We show
that $t$-AdaBoost retains AdaBoost's celebrated exponential convergence rate
when $t\in [0,1)$ while allowing a slight improvement of the rate's hidden
constant compared to $t=1$. $t$-AdaBoost partially computes on a generalization
of classical arithmetic over the reals and brings notable properties like
guaranteed bounded leveraging coefficients for $t\in [0,1)$. From the loss that
$t$-AdaBoost minimizes (a generalization of the exponential loss), we show how
to derive a new family of {\it tempered} losses for the induction of
domain-partitioning classifiers like decision trees. Crucially, strict
properness is ensured for all while their boosting rates span the full known
spectrum. Experiments using $t$-AdaBoost+trees display that significant
leverage can be achieved by tuning $t$.
