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Results 1 - 10
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514
Statistical Evaluation of a Bottom-Up Clustering for Single Particle Molecular Images
"... We examined the statistical performance of clustering single particle molecular images by bottom-up clustering, a hierarchical algorithm, using simulated protein images with a low signalto-noise ratio. Using covariance for the measure of similarity together with the iterative alignment, our method w ..."
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We examined the statistical performance of clustering single particle molecular images by bottom-up clustering, a hierarchical algorithm, using simulated protein images with a low signalto-noise ratio. Using covariance for the measure of similarity together with the iterative alignment, our method
Predictive Top-Down Knowledge Improves Neural Exploratory Bottom-Up Clustering
"... In this paper, we explore the hypothesis that integrating symbolic top-down knowledge into text vector representations can improve neural exploratory bottom-up representations for text clustering. By extracting semantic rules from WordNet, terms with similar concepts are substituted with a more ..."
Abstract
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Cited by 2 (0 self)
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In this paper, we explore the hypothesis that integrating symbolic top-down knowledge into text vector representations can improve neural exploratory bottom-up representations for text clustering. By extracting semantic rules from WordNet, terms with similar concepts are substituted with a more
A bottom-up clustering algorithm to detect ncRNA molecules with a
"... common secondary structure ..."
Statistical Evaluation of a Bottom-Up Clustering for Single Particle Molecular Images
"... uenoyt0ni.aist.go.jp isono0cbx-c. jp sltaka0cbrc. jp ..."
A Parallel Bottom-up Clustering Algorithm with Applications to Circuit Partitioning in VLSI Design
- In Proc. ACM/IEEE Design Automation Conference
, 1993
"... In this paper, we present a bottom-up clustering algorithm based on recursive collapsing of small cliques in a graph. The sizes of the small cliques are derived using random graph theory. This clustering algorithm leads to a natural parallel implementation in which multiple processors are used to id ..."
Abstract
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Cited by 67 (10 self)
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In this paper, we present a bottom-up clustering algorithm based on recursive collapsing of small cliques in a graph. The sizes of the small cliques are derived using random graph theory. This clustering algorithm leads to a natural parallel implementation in which multiple processors are used
Speaker Diarization using bottom-up clustering based on a Parameter-derived Distance between adapted GMMs
- in "Intl. Conf. on Speech and Language Processing
, 2004
"... In this paper, we present an approach for speaker diarization based on segmentation followed by bottom-up clustering, where clusters are modeled using adapted Gaussian mixture models. We propose a novel inter-cluster distance in the model parameter space which is easily computable and which can both ..."
Abstract
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Cited by 25 (2 self)
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In this paper, we present an approach for speaker diarization based on segmentation followed by bottom-up clustering, where clusters are modeled using adapted Gaussian mixture models. We propose a novel inter-cluster distance in the model parameter space which is easily computable and which can
Linguistic influences on bottom-up and top-down clustering for speaker diarization
- in Proc. Int. Conf. Acoustics, Speech, Signal Processing (ICASSP
"... While bottom-up approaches have emerged as the standard, default approach to clustering for speaker diarization we have always found the top-down approach gives equivalent or superior performance. Our recent work shows that significant gains in performance can be obtained when cluster purification i ..."
Abstract
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Cited by 3 (3 self)
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While bottom-up approaches have emerged as the standard, default approach to clustering for speaker diarization we have always found the top-down approach gives equivalent or superior performance. Our recent work shows that significant gains in performance can be obtained when cluster purification
Bottom-up Segmentation for Top-down Detection
"... In this paper we are interested in how semantic segmentation can help object detection. Towards this goal, we propose a novel deformable part-based model which exploits region-based segmentation algorithms that compute candidate object regions by bottom-up clustering followed by ranking of those reg ..."
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Cited by 30 (8 self)
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In this paper we are interested in how semantic segmentation can help object detection. Towards this goal, we propose a novel deformable part-based model which exploits region-based segmentation algorithms that compute candidate object regions by bottom-up clustering followed by ranking of those
Edge Separability Based Circuit Clustering with Application to Circuit Partitioning
- IEEE/ACM Asia South Pacific Design Automation Conference
, 2000
"... In this paper, we introduce a new efficient O(n log n) graph search based bottom-up clustering algorithm named ESC (Edge Separability based Clustering). Unlike existing bottom-up algorithms that are based on local connectivity information of the netlist, ESC exploits more global connectivity inform ..."
Abstract
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Cited by 45 (23 self)
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In this paper, we introduce a new efficient O(n log n) graph search based bottom-up clustering algorithm named ESC (Edge Separability based Clustering). Unlike existing bottom-up algorithms that are based on local connectivity information of the netlist, ESC exploits more global connectivity
ProtoNet: hierarchical classification of the protein space
- Nucleic Acids Res
, 2003
"... The ProtoNet site provides an automatic hierarchical clustering of the SWISS-PROT protein database. The clustering is based on an all-against-all BLAST similarity search. The similarities ’ E-score is used to perform a continuous bottom-up clustering process by applying alternative rules for merging ..."
Abstract
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Cited by 39 (12 self)
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The ProtoNet site provides an automatic hierarchical clustering of the SWISS-PROT protein database. The clustering is based on an all-against-all BLAST similarity search. The similarities ’ E-score is used to perform a continuous bottom-up clustering process by applying alternative rules
Results 1 - 10
of
514
