Now showing items 11-16 of 16
ur-CAIM: Improved CAIM Discretization for Unbalanced and Balanced Data
Supervised discretization is one of basic data preprocessing techniques used in data mining. CAIM (Class- Attribute InterdependenceMaximization) is a discretization algorithm of data for which the classes are known. ...
Parallel evaluation of Pittsburgh rule-based classifiers on GPUs
Individuals from Pittsburgh rule-based classifiers represent a complete solution to the classification problem and each individual is a variable-length set of rules. Therefore, these systems usually demand a high level ...
Speeding up Multiple Instance Learning Classification Rules on GPUs
Multiple instance learning is a challenging task in supervised learning and data mining. How- ever, algorithm performance becomes slow when learning from large-scale and high-dimensional data sets. Graphics processing ...
Scalable CAIM Discretization on Multiple GPUs Using Concurrent Kernels
CAIM(Class-Attribute InterdependenceMaximization) is one of the stateof- the-art algorithms for discretizing data for which classes are known. However, it may take a long time when run on high-dimensional large-scale ...
Speeding Up Evolutionary Learning Algorithms using GPUs
This paper propose a multithreaded Genetic Programming classi cation evaluation model using NVIDIA CUDA GPUs to reduce the computational time due to the poor perfor- mance in large problems. Two di erent clas- si ...
Parallelization Strategies for Markerless Human Motion Capture
Markerless Motion Capture (MMOCAP) is the problem of determining the pose of a person from images captured by one or several cameras simultaneously without using markers on the subject. Evaluation of the solutions is ...