In virtually all machine learning applications, hyper-parameter tuning is required to maximize predictive accuracy. Such tuning is computationally expensive, and the cost is further exacerbated by the need for multiple evaluations (via crossvalidation or bootstrap) at each configuration setting to guarantee statistically significant results. This paper presents a simple, general technique for i...

Most computer vision algorithms have parameters. This is a fact of life which is familiar to any researcher in the field. Unfortunately, for algorithms to work properly, the parameters have to be tuned. We propose a semi-automatic approach to parameter tuning, which is general-purpose and can be used for a wide variety of computer vision algorithms. The basic setup is as follows. The vision alg...

Hyper-parameter tuning is one of the crucial steps in the successful application of machine learning algorithms to real data. In general, the tuning process is modeled as an optimization problem for which several methods have been proposed. For complex algorithms, the evaluation of a hyper-parameter configuration is expensive and their runtime is speed up through data sampling. In this paper, t...

This paper addresses multidimensional tuning parameter selection in the context of “train-validate-test” and K-fold cross validation. A coarse grid search over tuning parameter space is used to initialize a descent method which then jointly optimizes over variables and tuning parameters. We study four regularized regression methods and develop the update equations for the corresponding descent ...

Parameter tuning is essential to generalization of support vector machine (SVM). Previous methods usually adopt a nested two-layer framework, where the inner layer solves a convex optimization problem, and the outer layer selects the hyper-parameters by minimizing either cross validation or other error bounds. In this paper, we propose a novel parameter tuning approach for SVM via kernel matrix...

Usually, human participation is required in order to provide feedback during the game tuning or balancing process. Moreover, this commonly an iterative process which play-testing as well interaction for gathering all important information improve and tune components’ specification. In paper, a mechanism proposed accelerate reduce significantly costs of it, contributing with solution p...