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544
Input and structure selection for k-nn approximator
- Hernandez (Eds.), Lecture Notes in Computer Science
, 2005
"... Abstract. This paper presents k-NN as an approximator for time series prediction problems. The main advantage of this approximator is its simplicity. Despite the simplicity, k-NN can be used to perform input selection for nonlinear models and it also provides accurate approximations. Three model str ..."
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Cited by 6 (4 self)
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Abstract. This paper presents k-NN as an approximator for time series prediction problems. The main advantage of this approximator is its simplicity. Despite the simplicity, k-NN can be used to perform input selection for nonlinear models and it also provides accurate approximations. Three model
kNN CF: A Temporal Social Network
- Proceedings of the 2008 ACM conference on Recommender systems
, 2008
"... Recommender systems, based on collaborative filtering, draw their strength from techniques that manipulate a set of userrating profiles in order to compute predicted ratings of unrated items. There are a wide range of techniques that can be applied to this problem; however, the k-nearest neighbour ( ..."
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Cited by 13 (4 self)
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explain why certain kNN parameters and similarity measures outperform others, and show that there is a surprising degree of structural similarity between these graphs and explicit user social networks.
Efficient k-NN Search on Vertically Decomposed Data
"... Applications like multimedia retrieval require e#cient support for similarity search on large data collections. Yet, nearest neighbor search is a di#cult problem in high dimensional spaces, rendering e#cient applications hard to realize: index structures degrade rapidly with increasing dimensionalit ..."
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Cited by 15 (2 self)
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dimensionality, while sequential search is not an attractive solution for repositories with millions of objects. This paper approaches the problem from a di#erent angle. A solution is sought in an unconventional storage scheme, that opens up a new range of techniques for processing k-NN queries, especially
Indexing Land Surface for Efficient kNN Query
"... The class of k Nearest Neighbor (kNN) queries is frequently used in geospatial applications. Many studies focus on processing kNN in Euclidean and road network spaces. Meanwhile, with the recent advances in remote sensory devices that can acquire detailed elevation data, the new geospatial applicati ..."
Abstract
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Cited by 5 (2 self)
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The class of k Nearest Neighbor (kNN) queries is frequently used in geospatial applications. Many studies focus on processing kNN in Euclidean and road network spaces. Meanwhile, with the recent advances in remote sensory devices that can acquire detailed elevation data, the new geospatial
Efficient k-NN Search on Vertically Decomposed Data
"... Applications like multimedia retrieval require efficient sup-port for similarity search on large data collections. Yet, nearest neighbor search is a difficult problem in high dimen-sional spaces, rendering efficient applications hard to realize: index structures degrade rapidly with increasing dimen ..."
Abstract
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dimension-ality, while sequential search is not an attractive solution for repositories with millions of objects. This paper approaches the problem from a different angle. A solution is sought in an unconventional storage scheme, that opens up a new range of techniques for processing k-NN queries
Improved Graph Based K-NN Text Classification
"... This paper presents an improved graph based k-nn algorithm for text classification. Most of the organization are facing problem of large amount of unorganized data. Most of the existing text classification techniques are based on vector space model which ignores the structural information of the doc ..."
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This paper presents an improved graph based k-nn algorithm for text classification. Most of the organization are facing problem of large amount of unorganized data. Most of the existing text classification techniques are based on vector space model which ignores the structural information
Modified K-NN model for stochastic streamflow simulation.
- Journal of Hydrologic Engineering
, 2006
"... Abstract This paper presents a modified k-nearest neighbor approach for streamflow generation. In this model, first, a local polynomial (a nonparametric function) is fitted to estimate the mean of the conditional probability density function. The simulation at any time point 't+1' given t ..."
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Cited by 5 (1 self)
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Abstract This paper presents a modified k-nearest neighbor approach for streamflow generation. In this model, first, a local polynomial (a nonparametric function) is fitted to estimate the mean of the conditional probability density function. The simulation at any time point 't+1' given
Bagging belief structures in Dempster-Shafer K-NN rule
"... This paper introduces bagging in the evidence-theoretic K-nearest neighbor rule (K-NN). It is known that bagging decreases the variability of classiers, so the main idea here is to build stable belief structures associated to query samples, before decisions are made. In order to compare bagge ..."
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Cited by 1 (0 self)
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This paper introduces bagging in the evidence-theoretic K-nearest neighbor rule (K-NN). It is known that bagging decreases the variability of classiers, so the main idea here is to build stable belief structures associated to query samples, before decisions are made. In order to compare
Efficiently Processing Continuous k-NN Queries on Data Streams
"... Efficiently processing continuous k-nearest neighbor queries on data streams is important in many application domains, e. g. for network intrusion detection or in querysubscriber systems. Usually not all valid data objects from the stream can be kept in main memory. Therefore, most existing solution ..."
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Cited by 17 (0 self)
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solutions immediately discard some of the objects and store only representative objects in an index. These solutions are thus approximative. In this paper, we propose an efficient method for exact k-NN monitoring. Our method is based on three ideas, (1) selecting exactly those objects from the stream which
Snake Table: A Dynamic Pivot Table for Streams of k-NN Searches
"... Abstract. We present the Snake Table, an index structure designed for supporting streams of k-NN searches within a content-based similarity search framework. The index is created and updated in the online phase while resolving the queries, thus it does not need a preprocessing step. This index is in ..."
Abstract
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Cited by 2 (1 self)
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Abstract. We present the Snake Table, an index structure designed for supporting streams of k-NN searches within a content-based similarity search framework. The index is created and updated in the online phase while resolving the queries, thus it does not need a preprocessing step. This index
Results 1 - 10
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544
