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48.203  Articles
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To reuse existing motion data and generate new motion, a method of human motion nonlinear dimensionality reduction and generation, based on fast adaptive scaled Gaussian process latent variable models, is proposed. Through statistical learn... see more

The problem of data dimensionality reduction for diagnostic and recognizing model construction is solved. The object of study is theprocess of data-driven diagnosis. The subject of study is the data reduction methods for diagnostic model construction on p... see more

Vectors are a key type of geospatial data, and their discretization, which involves solving the problem of generating a discrete line, is particularly important. In this study, we propose a method for constructing a discrete line mathematical model for a ... see more

In data mining problem solving it has to operate with a large amount of data samples. This entails a significant amount of time to process the data. Therefore, an urgent task is to reduce the dimensionality of the data samples. The aim of paper is to prov... see more

Due to the launch of new applications the behavior of Internet traffic is changing. Hackers are always looking for sophisticated tools to launch attacks and damage the services. Researchers have been working on intrusion detection techniques involving mac... see more

Starting from the notions of q-entailment and p-entailment, a two-dimensional notion of entailment is developed with respect to certain generalized q-matrices referred to as B-matrices. After showing that every purely monotonic singleconclusion consequenc... see more

The problem of training sample reduction is considered. A method for data reduction based on association rules is developed. The proposed method of training sample dimensionality reduction includes stages of reduction of instances, features and redundant ... see more

An efficient reservoir operation technique plays a very important role in improving the water resources and energy efficiency of reservoirs. In order to effectively avoid or alleviate the “curse of dimensionality” of Multi-dimensional Dynamic Programming ... see more

This paper discusses the performance of the K-Nearest Neighbor Algorithm with dimension reduction using Principal Component Analysis (PCA) in the case of diabetes disease classification. A large number of variables and data on the diabetes dataset require... see more

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