检索结果-内蒙古大学图书馆

Multimodal feature fusion for CNN-based gait recognition: An empirical comparison

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Castro, F.M. Marín-Jiménez, M.J. Guil, N. Pérez De La Blanca, N. Department of Computer Architecture University of Malaga 29071 Spain Department of Computing and Numerical Analysis University of Cordoba 14071 Spain Department of Computer Science and Artificial Intelligence University of Granada 18071 Spain

People identification in video based on the way they walk (i.e. gait) is a relevant task in computer vision using a non-invasive approach. Standard and current approaches typically derive gait signatures from sequences of binary energy maps of subjects extracted from images, but this process introduces a large amount of non-stationary noise, thus, conditioning their efficacy. In contrast, in this paper we focus on the raw pixels, or simple functions derived from them, letting advanced learning techniques to extract relevant features. Therefore, we present a comparative study of different Convolutional Neural Network (CNN) architectures on three low-level features (i.e. gray pixels, optical flow channels and depth maps) on two widely-adopted and challenging datasets: TUM-GAID and CASIA-B. In addition, we perform a comparative study between different early and late fusion methods used to combine the information obtained from each kind of low-level features. Our experimental results suggest that (i) the use of hand-crafted energy maps (e.g. GEI) is not necessary, since equal or better results can be achieved from the raw pixels;(ii) the combination of multiple modalities (i.e. gray pixels, optical flow and depth maps) from different CNNs allows to obtain state-of-the-art results on the gait task with an image resolution several times smaller than the previously reported results;and, (iii) the selection of the architecture is a critical point that can make the difference between state-of-the-art results or poor results. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

Chaos control in the fractional order logistic map via impulses

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Danca, Marius F. Fečkan, Michal Kuznetsov, Nikolay Dept. of Mathematics and Computer Science Avram Iancu University Cluj-Napoca400380 Romania Romanian Institute for Science and Technology Cluj-Napoca400487 Romania Dept. of Mathematical Analysis and Numerical Mathematics Comenius University in Bratislava Slovakia Mathematical Institute Slovak Academy of Sciences Slovakia Department of Applied Cybernetics Saint-Petersburg State University Russia Dept. of Mathematical Information Technology University of Jyväskylä Finland

In this paper the chaos control in the discrete logistic map of fractional order is obtained with an impulsive control algorithm. The underlying discrete initial value problem of fractional order is considered in terms of Caputo delta fractional difference. Every ∆ steps, the state variable is instantly modified with the same impulse value, chosen from a bifurcation diagram versus impulse. It is shown that the solution of the impulsive control is bounded. The numerical results are verified via time series, histograms, and the 0-1 test. Several examples are considered. Copyright © 2019, The Authors. All rights reserved.

关键词： Chaos control

Optimal pebbling number of graphs with given minimum degree

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Czygrinow, A. Hurlbert, G. Katona, G.Y. Papp, L.F. School of Mathematical and Statistical Sciences Arizona State University Department of Mathematics and Applied Mathematics Virginia Commonwealth University Department of Computer Science and Information Theory Budapest University of Technology and Economics MTA-ELTE Numerical Analysis and Large Networks Research Group Hungary Department of Computer Science and Information Theory Budapest University of Technology and Economics MTA-ELTE Numerical Analysis and Large Networks Research Group Hungary

Consider a distribution of pebbles on a connected graph G. A pebbling move removes two pebbles from a vertex and places one to an adjacent vertex. A vertex is reachable under a pebbling distribution if it has a pebble after the application of a sequence of pebbling moves. The optimal pebbling number π∗(G) is the smallest number of pebbles which we can distribute in such a way that each vertex is reachable. It was known that the optimal pebbling number of any connected graph is at most δ4+1n , where δ is the minimum degree of the graph. We strengthen this bound by showing that equality cannot be attained and that the bound is sharp. If diam(G) ≥ 3 then we further improve the bound to π∗(G) ≤ 3δ.75+1n . On the other hand, we show that for arbitrary large diameter and any ǫ > 0 there are infinitely many graphs whose optimal pebbling number is bigger n than (83 − ǫ) (δ+1). Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

Hyperbolic geometrical approach to model reduction

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2017年第1期50卷 12905-12910页

作者： Soumelidis, Alexandros Bokor, József Schipp, Ferenc Szabó, Zoltán Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary Institute for Computer Science and Control Hungarian Academy of Sciences MTA-BME Control Engineering Research Group Hungary Department of Numerical Analysis Eötvös Loránd University Budapest Hungary

Model reduction of large scale systems is an actively researched area of modelling and control. The problem is more involved if uncertainties are also present and a computational tractable nominal model is needed for the design. Based on results of the Kolmogorov n-width theory the paper provides useful bounds for the worst case approximation error - both H2and H∞– in terms of the hyperbolic distance related to the sets of uncertain poles. A related model reduction strategy that uses only this a priori pole information is also proposed. The method is illustrated through numerical examples. © 2017

关键词： numerical methods Computation theory Frequency domain analysis Large scale systems Poles Approximation errors Frequency domain identification Geometrical approaches Hyperbolic distances Identification for control Model reduction strategies Modelling and controls Nonparametric methods

Decomposition of a graph into two disjoint odd subgraphs

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Kano, Mikio Katona, Gyula Y Varga, Kitti Ibaraki University Hitachi Ibaraki Japan Department of Computer Science and Information Theory Budapest University of Technology and Economics Hungary MTA-ELTE Numerical Analysis and Large Networks Research Group Hungary

An odd (resp. even) subgraph in a multigraph is its subgraph in which every vertex has odd (resp. even) degree. We say that a multi- graph can be decomposed into two odd subgraphs if its edge set can be partitioned into two sets so that both form odd subgraphs. In this paper we give a necessary and sufficient condition for the decom- posability of a multigraph into two odd subgraphs. We also present a polynomial time algorithm for finding such a decomposition or showing its non-existence. We also deal with the case of the decomposability into an even subgraph and an odd *** Codes 05C70 Copyright © 2018, The Authors. All rights reserved.

关键词： Polynomial approximation

OCAPIS: R package for ordinal classification and preprocessing in scala

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Cristina Heredia-Gómez, M. García, Salvador Gutiérrez, Pedro Antonio Herrera, Francisco DaSCI Andalusian Institute of Data Science and Computational Intelligence University of Granada Spain Department of Computer Science and Numerical Analysis University of Córdoba Campus de Rabanales edificio Albert Einstein 14017 Córdoba Spain

Ordinal Data are those where a natural order exists between the labels. The classification and preprocessing of this type of data is attracting more and more interest in the area of machine learning, due to its presence in many common problems. Traditionally, ordinal classification problems have been approached as nominal problems. However, that implies not taking into account their natural order constraints. In this paper, an innovative R package named ocapis (Ordinal Classification and Preprocessing In Scala) is introduced. Implemented mainly in Scala and available through Github, this library includes four learners and two preprocessing algorithms for ordinal and monotonic data. Main features of the package and examples of installation and use are explained throughout this manuscript. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

Time series clustering based on the characterisation of segment typologies

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Guijo-Rubio, David Durán-Rosal, Antonio Manuel Gutiérrez, Pedro Antonio Troncoso, Alicia Hervás-Martínez, César Department of Computer Science and Numerical Analysis University of Córdoba Rabanales Campus Albert Einstein Building 3rd Floor Córdoba14071 Spain Department of Computer Languages and Systems University Pablo de Olavide Sevilla Spain

—Time series clustering is the process of grouping time series with respect to their similarity or characteristics. Previous approaches usually combine a specific distance measure for time series and a standard clustering method. However, these approaches do not take the similarity of the different subsequences of each time series into account, which can be used to better compare the time series objects of the dataset. In this paper, we propose a novel technique of time series clustering based on two clustering stages. In a first step, a least squares polynomial segmentation procedure is applied to each time series, which is based on a growing window technique that returns different-length segments. Then, all the segments are projected into same dimensional space, based on the coefficients of the model that approximates the segment and a set of statistical features. After mapping, a first hierarchical clustering phase is applied to all mapped segments, returning groups of segments for each time series. These clusters are used to represent all time series in the same dimensional space, after defining another specific mapping process. In a second and final clustering stage, all the time series objects are grouped. We consider internal clustering quality to automatically adjust the main parameter of the algorithm, which is an error threshold for the segmentation. The results obtained on 84 datasets from the UCR Time Series Classification Archive have been compared against two state-of-the-art methods, showing that the performance of this methodology is very promising. Copyright © 2018, The Authors. All rights reserved.

关键词： Time series

The VMS workshop series

学校读者我要写书评

暂无评论

SeMA Journal 2018年第4期75卷 569-571页

作者： Chacón Rebollo, Tomás John, Volker Lube, Gert Department EDAN and IMUS University of Seville C/Tarfia s/n. Seville 41012 Spain Weierstrass Institute for Applied Analysis and Stochastics Leibniz Institute in Forschungsverbund Berlin e. V. (WIAS) Mohrenstr. 39 Berlin 10117 Germany Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 6 Berlin 14195 Germany Institute for Numerical and Applied Mathematics Georg-August-University Göttingen Göttingen 37083 Germany

Monotonic classification: An overview on algorithms, performance measures and data sets

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Cano, José-Ramón Gutiérrez, Pedro Antonio Krawczyk, Bartosz Woźniak, Michal García, Salvador Dept. of Computer Science University of Jaén EPS of Linares Avenida de la Universidad S/N Linares Jaén23700 Spain Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Department of Computer Science Virginia Commonwealth University RichmondVA23284 United States Department of Computer Science Wroclaw University of Technology Wyb. Wyspiańskiego 27 Wroclaw50-370 Poland Department of Computer Science and Artificial Intelligence University of Granada Granada18071 Spain

Currently, knowledge discovery in databases is an essential step to identify valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfill restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently, there is a growing interest from the data mining research community concerning monotonic predictive models. This paper aims to present an overview about the literature in the field, analyzing existing techniques and proposing a taxonomy of the algorithms based on the type of model generated. For each method, we review the quality metrics considered in the evaluation and the different data sets and monotonic problems used in the analysis. In this way, this paper serves as an overview of the research about monotonic classification in specialized literature and can be used as a functional guide of the field. Copyright © 2018, The Authors. All rights reserved.

关键词： Data mining