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335
An Effective Indexing Method for High Dimensional Databases
"... Abstract – A large number of database applications like business data warehouses and scientific data repositories deal with highdimensional data sets. As the number of dimensions/attributes and the overall size of data sets increase, it becomes prime important to efficiently retrieve specific queri ..."
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Abstract – A large number of database applications like business data warehouses and scientific data repositories deal with highdimensional data sets. As the number of dimensions/attributes and the overall size of data sets increase, it becomes prime important to efficiently retrieve specific
Mathematical & Statistical Techniques for Dimension Reduction of High Dimensional Data: A Selective Survey
"... Abstract It is found from studies that the sizes of realworld databases are usually very large. This is basically due to the excessive number of features or due to the huge number of instances or both. It may not give appropriate results on analysing the data considering this excessive feature spac ..."
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space. Reduction in the data size can help the data mining tools to work efficiently. One such type of techniques is reducing the size by decreasing the number of features for easy and efficient analysis of data. There are different ways for achieving this goal, such as determine the relative importance
APPROXIMATION OF VOLUME AND BRANCH SIZE DISTRIBUTION OF TREES FROM LASER SCANNER DATA
"... This paper presents an approach for automatically approximating the aboveground volume and branch size distribution of trees from dense terrestrial laser scanner produced point clouds. The approach is based on the assumption that the point cloud is a sample of a surface in 3D space and the surface ..."
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Cited by 3 (1 self)
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is locally like a cylinder. The point cloud is covered with small neighborhoods which conform to the surface. Then the neighborhoods are characterized geometrically and these characterizations are used to classify the points into trunk, branch, and other points. Finally, proper subsets are determined
A variance minimization criterion to feature selection using laplacian regularization
 IEEE Transactions on Pattern Analysis and Machine Intelligence
"... Abstract—In many information processing tasks, one is often confronted with very highdimensional data. Feature selection techniques are designed to find the meaningful feature subset of the original features which can facilitate clustering, classification, and retrieval. In this paper, we consider ..."
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Cited by 13 (1 self)
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Abstract—In many information processing tasks, one is often confronted with very highdimensional data. Feature selection techniques are designed to find the meaningful feature subset of the original features which can facilitate clustering, classification, and retrieval. In this paper, we consider
Network Optimizations for Large Vocabulary Speech Recognition
 Speech Communication
, 1998
"... The redundancy and the size of networks in largevocabulary speech recognition systems can have a critical effect on their overall performance. We describe the use of two new algorithms: weighted determinization and minimization [12]. These algorithms transform recognition labeled networks into equi ..."
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Cited by 23 (6 self)
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The redundancy and the size of networks in largevocabulary speech recognition systems can have a critical effect on their overall performance. We describe the use of two new algorithms: weighted determinization and minimization [12]. These algorithms transform recognition labeled networks
1 MeanField Theory of MetaLearning
"... We discuss here the meanfield theory for a cellular automata model of metalearning. The metalearning is the process of combining outcomes of individual learning procedures in order to determine the final decision with higher accuracy than any single learning method. Our method is constructed from ..."
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We discuss here the meanfield theory for a cellular automata model of metalearning. The metalearning is the process of combining outcomes of individual learning procedures in order to determine the final decision with higher accuracy than any single learning method. Our method is constructed
The objective of this exercise is to learn A*.
"... The problem of planning a path given a start and a goal position is called the path planning problem. We often encounter such problems in the area of mobile robotics where the goal is to give the robot the ability to determine how it should navigate from its current location to some desired goal pos ..."
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that the numerous characters follow in a game.) There are numerous existing methods that can be employed to solve the path planning problem. A common approach is to tesselate the workspace into cells. Once the workspace has been properly discretized, one can represent the layout of the workspace using what
Spectral Methods for Semisupervised Manifold Learning
"... Given a finite number of data points sampled from a lowdimensional manifold embedded in a high dimensional space together with the parameter vectors for a subset of the data points, we need to determine the parameter vectors for the rest of the data points. This problem is known as semisupervised ..."
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Cited by 2 (0 self)
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Given a finite number of data points sampled from a lowdimensional manifold embedded in a high dimensional space together with the parameter vectors for a subset of the data points, we need to determine the parameter vectors for the rest of the data points. This problem is known as semi
Results 11  20
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335