Several machine learning algorithms have been applied to the problem of static hand posture recognition. K-nearesr neighbor (KNN) performs very well in flexible posture recognition, but speed and memory requirements of the algorithm make it difficult to use in real time applications. In this paper we propose an approach to speed up the KNN without changing its behavior. We use the mixture of ga...

Random KNN (RKNN) is a novel generalization of traditional nearest-neighbor modeling. Random KNN consists of an ensemble of base k-nearest neighbor models, each constructed from a random subset of the input variables. A collection of r such base classifiers is combined to build the final Random KNN classifier. Since the base classifiers can be computed independently of one another, the overall ...

A k-nearest neighbor (kNN) query, which retrieves nearest k points from a database is one of the fundamental query types in spatial databases. An all k-nearest neighbor query (AkNN query), a variation of a kNN query, determines the k-nearest neighbors for each point in the dataset in a query process. In this paper, we propose a method for processing AkNN queries in Hadoop. We decompose the give...

The nearest neighbor (NN) technique is very simple, highly efficient and effective in the field of pattern recognition, text categorization, object recognition etc. Its simplicity is its main advantage, but the disadvantages can’t be ignored even. The memory requirement and computation complexity also matter. Many techniques are developed to overcome these limitations. NN techniques are broadly...

Lead-free (K,Na)NbO3 (KNN) and La doped (K,Na)NbO3 (KNN-La) thin films were grown on SrTiO3 substrates by chemical solution deposition method. The effect of adding different amounts of Na and K excess (0-20 mol%) has been investigated. The results confirm the necessity to add 20 mol% excess amounts of Na and K precursor solutions in order to avoid the formation of the secondary phase, K4Nb6O17,...

proper forest management needs quantitative and precise estimates of forest stands characteristics. remotely sensed imageries, due to accurate and broad spatial information, has become a cost-effective tool in forest management. classification of forest attributes and generation of thematic maps are among the common applications of remote sensing. the objective of this study was to optimize the...

In this project we present a heuristic learning process by training ANN (artificial neural network) and KNN (k-nearest neighbor) using the best number of steps, gained from A*, from randomly generated states to the goal. After training ANN and KNN, the mixture of Experts is discussed and the empirical data are collected to demonstrate the feasibility and accuracy of combination of ANN and KNN i...

k nearest neighbor join (kNN join), designed to find k nearest neighbors from a dataset S for every object in another dataset R, is a primitive operation widely adopted by many data mining applications. As a combination of the k nearest neighbor query and the join operation, kNN join is an expensive operation. Given the increasing volume of data, it is difficult to perform a kNN join on a centr...

In this paper, a new lead-free piezoelectric (K,Na)NbO3 (KNN) film is presented as a promising, environment-friendly alternative to the conventional piezoelectric thin film materials like PZT, etc. with regard to applying into piezo-MEMS devices in general and micro-energy-harvesting devices in particular. The KNN films deposited by the RF magnetron sputtering deposition system were revealed ex...

The standard Gaussian process (GP) regression is often intractable when a data set is large or spatially nonstationary. In this paper, we address these challenging data properties by designing a novel K nearest neighbor based Kalman filter Gaussian process (KNN-KFGP) regression. Based on a state space model established by the KNN driven data grouping, our KNN-KFGP recursively filters out the la...