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7,183
Using Graphics Hardware for Multiple Datasets
"... We have applied three graphics hardware-based approaches to support concurrent visualization of multiple sets of volumetric scalar data. They include volume rendering, clipping and isosurface extraction methods, which exploit 3D textures and advanced per pixel operations. These methods are expected ..."
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to give better interactive frame rates for multiple datasets visualization (MDV) compared to the software-based methods. The rendering time in each case increases nonlinearly with the increasing the number (N) of the datasets being visualized. We can identify three regimes, which can be characterized
selection variables from multiple datasets
"... equivalence classes of acyclic models with latent and ..."
A Technique to Association Rule Mining on Multiple Datasets
"... Abstract — This research aims at studying the method for association rule mining on multiple datasets. Current with technology and information systems enabling agencies or organization has a data-storage system, but the problem is that those with a larger data set, which is difficult in the associat ..."
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Abstract — This research aims at studying the method for association rule mining on multiple datasets. Current with technology and information systems enabling agencies or organization has a data-storage system, but the problem is that those with a larger data set, which is difficult
Bayesian correlated clustering to integrate multiple datasets
, 2012
"... Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern highthroughput technologies generate a broad array of different data types, providing distinct – but often complementary – information. We present a Bayesian method for the unsupe ..."
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Cited by 10 (1 self)
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Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern highthroughput technologies generate a broad array of different data types, providing distinct – but often complementary – information. We present a Bayesian method
A Rough Set Approach to Multiple Dataset Analysis
"... In the area of data mining, the discovery of valuable changes and connections (e.g., causality) from multiple data sets has been recognized as an important issue. This issue essentially differs from finding statistical associations in a single data set because it is complicated by the different data ..."
Abstract
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Cited by 2 (1 self)
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set theory, fuzzy theory, multiple datasets, causality 1.
Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions
- Journal of Machine Learning Research
, 2002
"... This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings. We first identify several application scenarios for the resultant 'knowledge reuse&ap ..."
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Cited by 603 (20 self)
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qualitatively different application scenarios: (i) where the original clusters were formed based on non-identical sets of features, (ii) where the original clustering algorithms worked on non-identical sets of objects, and (iii) where a common data-set is used and the main purpose of combining multiple
LabelMe: A Database and Web-Based Tool for Image Annotation
, 2008
"... We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant sha ..."
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Cited by 679 (46 self)
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sharing of such annotations. Using this annotation tool, we have collected a large dataset that spans many object categories, often containing multiple instances over a wide variety of images. We quantify the contents of the dataset and compare against existing state of the art datasets used for object
Fitting a mixture model by expectation maximization to discover motifs in biopolymers.
- Proc Int Conf Intell Syst Mol Biol
, 1994
"... Abstract The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expect~tiou ma.,dmization to fit a two-component finite mixture model to the set of sequences. Multiple motifs are found by fitting a mixture model to th ..."
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Cited by 947 (5 self)
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Abstract The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expect~tiou ma.,dmization to fit a two-component finite mixture model to the set of sequences. Multiple motifs are found by fitting a mixture model
Activity recognition from user-annotated acceleration data
, 2004
"... In this work, algorithms are developed and evaluated to detect physical activities from data acquired using five small biaxial accelerometers worn simultaneously on different parts of the body. Acceleration data was collected from 20 subjects without researcher supervision or observation. Subjects ..."
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Cited by 515 (7 self)
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performance recognizing everyday activities with an overall accuracy rate of 84%. The results show that although some activities are recognized well with subject-independent training data, others appear to require subject-specific training data. The results suggest that multiple accelerometers aid
On the algorithmic implementation of multi-class kernel-based vector machines
- Journal of Machine Learning Research
"... In this paper we describe the algorithmic implementation of multiclass kernel-based vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic ob ..."
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Cited by 559 (13 self)
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objective function. Unlike most of previous approaches which typically decompose a multiclass problem into multiple independent binary classification tasks, our notion of margin yields a direct method for training multiclass predictors. By using the dual of the optimization problem we are able
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