part 2

Comparing our block-coordinate descent algorithm in its kernel version with a gradient descent approach similar to the one used for multiple kernel learning in SimpleMKL.. The

This local optimization strategy, compared to standard gradient-based methods such as the steepest-descent or nonlinear conjugate gradient methods, or quasi-Newton methods such as

1) Gradient method: One of the classic local search tech- nique is gradient descent. It is based on the analysis direction of search space by the calculating the negative gradient

Keywords: pairwise coupling, stochastic gradient descent, support vec- tor machine, multiclass learning, large datasets..

• We propose an Anderson acceleration scheme for coordinate descent, which, as visible on Figure 1 , outperforms inertial and extrapolated gradient descent, as well as

The many advantages of using stochastic gradient descent algorithms, like the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, to approach a sensor-based

Bridging the gap between constant step size stochastic gradient descent and markov chains.. Conditional gradient algorithms with open loop step

Statistical optimality of stochastic gradient descent on hard learning problems through multiple passes. Learning with incremental