In order to deal with known limitations of the hard margin support vector machine (SVM) for binary classification — such as overfitting and the fact that some data sets are not linearly separable —, a soft margin approach has been proposed in literature [2, 4, 5]. The soft margin SVM allows training data to be misclassified to a certain extent, by introducing slack variables and penalizing the ...

A common practice is to design a controller by plant observations (i.e. experiments) and to optimize some of its parameters by trial-and-error. This paper proposes a genetic algorithm for the automation of the search procedure and its implementation on a programmable logic controller. The details of this implementation will be discussed along with an example one carried out for the control of a...

We describe a novel method for tuning the decoding parameters of a speech-to-text system so as to minimize word error rate (WER) subject to an over-all time constraint. When applied to three sub-realtime systems for recognizing English conversational telephone speech, the method gave speed improvements of up to 21.1% while at the same time reducing WER by up to 6.7%.

Genetic Algorithms (GA) is a family of search algorithms based on the mechanics of natural selection and biological evolution. They are able to efficiently exploit historical information in the evolution process to look for optimal solutions or approximate them for a given problem, achieving excellent performance in optimization problems that involve a large set of dependent variables. Despite ...

This paper presents a learning-based method for parameter tuning of object recognition systems and its application to automatic road extraction from high resolution remotely sensed (HRRS) images. Our approach is based on region growing using fast marching level set method (FMLSM), and machine learning for automatically tuning its parameters. FMLSM is used to extract the shape of objects in imag...

The non-stationary nature of image characteristics calls for adaptive processing, based on the local image content. We propose a simple and flexible method to learn local tuning of parameters in adaptive image processing: we extract simple local features from an image and learn the relation between these features and the optimal filtering parameters. Learning is performed by optimizing a user d...

As future HPC systems become larger, the failure rates and the cost of checkpointing to the global file system are expected to increase. To solve this problem, this paper proposes a hierarchical incremental CPR mechanism that utilizes a hierarchical storage system of local storages and global storages. Then, to adjust the parameters of the proposed mechanism, a runtime autotuning technique is p...

Computationally challenging problems arise in the context of many applications, and the ability to solve these as efficiently as possible is of great practical, and often also economical importance. Examples of such problems include scheduling, timetabling, resource allocation, production planning and optimisation, computer-aided design and software verification. Many of these problems are NP-h...

Parameter tuning is a fundamental problem that has to be handled by any Big Data analytics system. Identifying the optimal model parameters is an interactive, human-in-the-loop process that requires many hours – if not days and months – even for experienced data scientists. We argue that the incapacity to evaluate multiple parameter configurations simultaneously and the lack of support to quick...

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