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213
TROPICAL FOREST BIOMASS ESTIMATION AND MAPPING USING KNEAREST NEIGHBOUR (KNN) METHOD
"... Estimation and mapping of tropical forest biomass is important for periodic carbon accounting, as tropical deforestation is one of the major sources of terrestrial carbon emission in the recent decades. Knearest neighbour (kNN) method is recently introduced for the estimation of boreal and temperat ..."
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Estimation and mapping of tropical forest biomass is important for periodic carbon accounting, as tropical deforestation is one of the major sources of terrestrial carbon emission in the recent decades. Knearest neighbour (kNN) method is recently introduced for the estimation of boreal
WEIGTHED KNN
"...  herein is presented the comparison between several class prediction methods – the K Nearest Neighbour (KNN) algorithms and some variations of it – for classification of tumours using gene expression data (“MITLeukemia ” data set is used, and it contains the expressions levels of 7129 genes in 72 m ..."
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 herein is presented the comparison between several class prediction methods – the K Nearest Neighbour (KNN) algorithms and some variations of it – for classification of tumours using gene expression data (“MITLeukemia ” data set is used, and it contains the expressions levels of 7129 genes in 72
KNN ModelBased Approach in Classification
, 2003
"... The kNearestNeighbours (kNN) is a simple but effective method for classification. The major drawbacks with respect to kNN are (1) its low efficiency  being a lazy learning method prohibits it in many applications such as dynamic web mining for a large repository, and (2) its dependency on the ..."
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Cited by 5 (1 self)
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The kNearestNeighbours (kNN) is a simple but effective method for classification. The major drawbacks with respect to kNN are (1) its low efficiency  being a lazy learning method prohibits it in many applications such as dynamic web mining for a large repository, and (2) its dependency
Extending fast nearest neighbour search algorithms for approximate kNN classification. Pattern Recognition and Image Analysis
 Lecture Notes in Computer Science, F.J. Perales et al (Eds
, 2003
"... Abstract. The nearest neighbour (NN) and knearest neighbour (kNN) classication rules have been widely used in pattern recognition due to its simplicity and good behaviour. Exhaustive nearest neighbour search can become unpractical when facing large training sets, high dimensional data or expensiv ..."
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Cited by 4 (3 self)
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Abstract. The nearest neighbour (NN) and knearest neighbour (kNN) classication rules have been widely used in pattern recognition due to its simplicity and good behaviour. Exhaustive nearest neighbour search can become unpractical when facing large training sets, high dimensional data
Extending LAESA fast nearest neighbour algorithm to find the k nearest neighbours
, 2002
"... Many pattern recognition tasks make use of the k nearest neighbour (kNN) technique. In this paper we are interested on fast k NN search algorithms that can work in any metric space i.e. they are not restricted to Euclideanlike distance functions. Only symmetric and triangle inequality propertie ..."
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Cited by 1 (1 self)
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Many pattern recognition tasks make use of the k nearest neighbour (kNN) technique. In this paper we are interested on fast k NN search algorithms that can work in any metric space i.e. they are not restricted to Euclideanlike distance functions. Only symmetric and triangle inequality
A modification of the LAESA algorithm for approximated kNN classification
, 2003
"... Nearestneighbour (NN) and knearestneighbours (kNN) techniques are widely used in many Pattern Recognition classi cation tasks. The LAESA is a fast nearestneighbour algorithm which does not assume that the prototypes are de ned in a vector space; it only makes use of some of the distance proper ..."
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Cited by 11 (3 self)
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Nearestneighbour (NN) and knearestneighbours (kNN) techniques are widely used in many Pattern Recognition classi cation tasks. The LAESA is a fast nearestneighbour algorithm which does not assume that the prototypes are de ned in a vector space; it only makes use of some of the distance
A Variable Metric Probabilistic kNearestNeighbours Classifier
"... The knearest neighbour (knn) model is a simple, popular classifier. Probabilistic knn is a more powerful variant in which the model is cast in a Bayesian framework using (reversible jump) Markov chain Monte Carlo methods to average out the uncertainy over the model parameters. ..."
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Cited by 2 (0 self)
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The knearest neighbour (knn) model is a simple, popular classifier. Probabilistic knn is a more powerful variant in which the model is cast in a Bayesian framework using (reversible jump) Markov chain Monte Carlo methods to average out the uncertainy over the model parameters.
Extensions of the k nearest neighbour methods for classification problems
 in Proc. of 26th IASTED International Conference on Artificial Intelligence and Applications, CD Proceedings ISBN: 9780889867109, 2008
"... The k Nearest Neighbour (kNN) method is a widely used technique which has found several applications in clustering and classification. In this paper, we focus on classification problems and we propose modifications of the nearest neighbour method that exploit information from the structure of a data ..."
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Cited by 3 (2 self)
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The k Nearest Neighbour (kNN) method is a widely used technique which has found several applications in clustering and classification. In this paper, we focus on classification problems and we propose modifications of the nearest neighbour method that exploit information from the structure of a
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 knearest neighbour ( ..."
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Cited by 13 (4 self)
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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 knearest neighbour
A fast approximately knearestneighbour search algorithm for classification tasks
, 2000
"... The knearestneighbour (kNN) search algorithm is widely used in pattern classification tasks. A large set of fast kNN search algorithms have been developed in order to obtain lower error rates. Most of them are extensions of fast NN search algorithms where the condition of finding exactly the ..."
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The knearestneighbour (kNN) search algorithm is widely used in pattern classification tasks. A large set of fast kNN search algorithms have been developed in order to obtain lower error rates. Most of them are extensions of fast NN search algorithms where the condition of finding exactly
Results 1  10
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213