This paper introduces a new local asymmetric weighting scheme for the nearest neighbor classiication algorithm. It is shown both with theoretical arguments and computer experiments that good compression rates can be achieved outperforming the accuracy of the standard nearest neighbor classiication algorithm and obtaining almost the same accuracy as the k-NN algorithm with k optimised in each da...

Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...

This paper presents an interval type-2 fuzzy Knearest neighbor (NN) algorithm that is an extension of the type1 fuzzy K-NN algorithm proposed in [l]. In our proposed method, the membership values for each vector are 'extended as interval type-2 fuzzy memberships by assigning uncertainty to the type-1 memberships. By doing so, the classification result obtained by the interval type-2 fuzzy K-NN ...

This paper proposes a novel k-nearest neighbor algorithm to predict soil moisture in maize field. In order to estimate soil moisture in maize field accurately without any destruction to root and soil, this paper uses biological characteristics of maize to estimate soil moisture, including plant height, leaf area, stem diameter, dry weight and fresh weight, all the values of which are non-negati...

in different projects the speed of different machinery can be estimated using manufacturer's handbooks and a number of modification factors to consider the environmental effects, type of the project and status of site management. since the statuses of different factors of the domestic projects are totally different from those of the international projects, there is a wide discrepancy betwe...

This paper proposes a new approach to object searching in video databases, SoftCBIR, which combines a keypoint matching algorithm and a graduated assignment algorithm based on softassign. Compared with previous approaches, SoftCBIR is an innovative combination of two powerful techniques: 1) An energy minimization algorithm is applied to match two groups of keypoints while accounting for both th...

For many computer vision problems, the most time consuming component consists of nearest neighbor matching in high-dimensional spaces. There are no known exact algorithms for solving these high-dimensional problems that are faster than linear search. Approximate algorithms are known to provide large speedups with only minor loss in accuracy, but many such algorithms have been published with onl...

This paper proposes a new approach to object searching in video databases, SoftCBIR, which combines a keypoint matching algorithm and a graduated assignment algorithm based on softassign. Compared with previous approaches, SoftCBIR is an innovative combination of two powerful techniques: 1) An energy minimization algorithm is applied to match two groups of keypoints while accounting for both th...

We consider the problem of recovering clustered sparse signals with no prior knowledge of the sparsity pattern. Beyond simple sparsity, signals of interest often exhibits an underlying sparsity pattern which, if leveraged, can improve the reconstruction performance. However, the sparsity pattern is usually unknown a priori. Inspired by the idea of k-nearest neighbor (k-NN) algorithm, we propose...

This paper presents an algorithm, called the winnerupdate algorithm, for accelerating the nearest neighbor search. By constructing a hierarchical structure for each feature point in the lp metric space, this algorithm can save a large amount of computation at the expense of moderate preprocessing and twice the memory storage. Given a query point, the cost for computing the distances from this p...