Abstract Parallel processing of large spatial datasets over distributed systems has become a core part modern data analytic like Apache Hadoop and Spark. The general-purpose design these does not natively account for the data’s attributes results in poor scalability, accuracy, or prolonged runtimes. Spatial extensions remedy problem introduce recognition operations. At extension, locality-prese...

We introduce an extension of the modified value-difference kernel of k-nn by replacing the kernel’s default class distribution matrix with the matrix produced by the maximum-entropy learning algorithm. This hybrid algorithm is tested on fifteen machine learning benchmark tasks, comparing the hybrid to standard k-nn classification and maximum-entropy-based classification. Results show that the h...

the aim of this work is to examine the feasibilities of the support vector machines (svms) and k-nearest neighbor (k-nn) classifier methods for the classification of an aquifer in the khuzestan province, iran. for this purpose, 17 groundwater quality variables including ec, tds, turbidity, ph, total hardness, ca, mg, total alkalinity, sulfate, nitrate, nitrite, fluoride, phosphate, fe, mn, cu, ...

In this work, we propose a new technique for fast k-nearest neighbor (k-NN) classification in which the original database is represented via a small set of learned binary prototypes. The training phase simultaneously learns a hash function which maps the data points to binary codes, and a set of representative binary prototypes. In the prediction phase, we first hash the query into a binary cod...

With the large volume of electronic portal images acquired and stringent time constraints, it is no longer feasible to follow the convention whereby the radiation oncologist reviews and approves or rejects all portals. For that purpose we have developed a portal image classifier based on the fuzzy k-nearest neighbour (k-NN) algorithm. Each portal image is represented by a feature vector that co...

In this paper, we propose a method for multi-class pattern classification by combined use of Multinomial Logit Model (MLM) and K-nearest neighbor rule (KNN). Multinomial Logit Model (MLM) is one of the neural network models for multi-class pattern classification, and is supposed to be equal or better in class&cation performance than linear classification methods. K-NN is a simple but powerful n...

Citation: ter Braak, C. J. F. (1995). Non-linear methods for multivariate statistical calibration and their use in palaeoecology: a comparison of inverse (k-nearest neighbours, partial least squares and weighted averaging partial least squares) and classical approaches. Abstract Current environmental problems, such as acid rain and global warming, have greatly increased interest in fossil speci...