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2,568
Towards Parallel Classification of TBoxes
"... Abstract. One of the most frequently used inference services of description logic reasoners is the classification of TBoxes with a subsumption hierarchy of all named concepts as the result. In response to (i) emerging TBoxes from the semantic web community consisting of up to hundreds of thousand of ..."
Abstract
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Cited by 5 (0 self)
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of named concepts and (ii) the increasing availability of multi-processor and multi- or many-core computers, we propose a parallel approach for TBox classification. First experiments on parallelizing well-known algorithms for TBox classification were conducted to study the trade-off between incompleteness
Parallel classification on SMP systems
- In The 1st Workshop on High Performance Data Mining (in conjuction with IPPS'98
, 1998
"... This paper presents fast scalable decision-tree-based classification algorithms targeting shared-memory systems. The algorithms are based on the sequential SPRINT classifier and span the gamut of data and task parallelism. The data parallelism is based on attribute scheduling among processors. This ..."
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Cited by 3 (0 self)
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This paper presents fast scalable decision-tree-based classification algorithms targeting shared-memory systems. The algorithms are based on the sequential SPRINT classifier and span the gamut of data and task parallelism. The data parallelism is based on attribute scheduling among processors
Parallel Classification on Shared-Memory Systems
"... An important task of data mining can be thought of as the process of assigning things to predefined categories or classes – a process called Classification. Since the classes are predefined this is also known as Supervised Induction. The input for the classification system consists of a set of examp ..."
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An important task of data mining can be thought of as the process of assigning things to predefined categories or classes – a process called Classification. Since the classes are predefined this is also known as Supervised Induction. The input for the classification system consists of a set
Parallel Classification for Data Mining on Shared-Memory Multiprocessors
, 1998
"... We present parallel algorithms for building decision-tree classifiers on shared-memory multiprocessor (SMP) systems. The proposed algorithms span the gamut of data and task parallelism. The data parallelism is based on attribute scheduling among processors. This basic scheme is extended with task pi ..."
Abstract
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Cited by 34 (2 self)
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We present parallel algorithms for building decision-tree classifiers on shared-memory multiprocessor (SMP) systems. The proposed algorithms span the gamut of data and task parallelism. The data parallelism is based on attribute scheduling among processors. This basic scheme is extended with task
Efficient Parallel Classification Using Dimensional Aggregates
- In Proceedings of Workshop on Large-Scala Parallel KDD Systems, with ACM SIGKDD-99
, 1999
"... Multidimensional aggregates are frequently computed to improve query performance in Online Analytical Processing applications. We present a new method for decision tree based classification trees using the aggregates computed in the multidimensional data model. The structure imposed on data in a exp ..."
Abstract
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Cited by 8 (1 self)
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-dimensional aggregates if the cell values are the class-id values, and counts are maintained for each class. This is used repeatedly at the nodes of the decision tree to calculate splits and manage data. Previous parallel approaches for decision-tree based classification use sorted attribute lists and hash tables
Efficient Parallel Classification Using Dimensional Aggregates
- In Proceedings of Workshop on Large-Scala Parallel KDD Systems, with ACM SIGKDD-99
, 1999
"... Multidimensional aggregates are frequently computed to improve query performance in Online Analytical Processing applications. We present a new method for decision tree based classification trees using the aggregates computed in the multidimensional data model. The structure imposed on data in a exp ..."
Abstract
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-dimensional aggregates if the cell values are the class-id values, and counts are maintained for each class. This is used repeatedly at the nodes of the decision tree to calculate splits and manage data. Previous parallel approaches for decision-tree based classification use sorted attribute lists and hash tables
Discriminant adaptive nearest neighbor classification,
, 1995
"... Abstract Nearest neighbor classification expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensions We propose a locally adaptive form of nearest neighbor classification to try to finesse this curse of dimensionality. We use a local linear discrimin ..."
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Cited by 321 (1 self)
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Abstract Nearest neighbor classification expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensions We propose a locally adaptive form of nearest neighbor classification to try to finesse this curse of dimensionality. We use a local linear
SPRINT: A scalable parallel classifier for data mining
, 1996
"... Classification is an important data mining problem. Although classification is a well-studied problem, most of the current classi-fication algorithms require that all or a por-tion of the the entire dataset remain perma-nently in memory. This limits their suitability for mining over large databases. ..."
Abstract
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Cited by 312 (8 self)
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. We present a new decision-tree-based classification algo-rithm, called SPRINT that removes all of the memory restrictions, and is fast and scalable. The algorithm has also been designed to be easily parallelized, allowing many processors to work together to build a single consistent model
Schema abstraction‖ in a multiple-trace memory model
- Psychological Review
, 1986
"... A simulation model of episodic memory, MINERVA 2, is applied to the learning of concepts, as represented bythe schema-abstraction task. The model assumes that each experience produces a separate memory trace and that knowledge of abstract oncepts i derived from the pool of episodic traces at the tim ..."
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Cited by 359 (2 self)
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at the time of retrieval. A retrieval cue contacts all traces imultaneously, activating each according to its similarity to the cue, and the information retrieved from memory reflects the summed content of all activated traces responding in parallel. The MINERVA 2 model is able to retrieve an abstracted
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