New Frontiers in Mining Complex Patterns

Res. 8, 1659–1685 (2007) 6. Boullé, M.: Functional data clustering via piecewise
constant nonparametric density estimation. Pattern Recogn. 45(12), 4389–4401 (
2012) 7. Buza, K.A.: Fusion methods for time-series classification. Ph.D. thesis ...

New Frontiers in Mining Complex Patterns

Author: Annalisa Appice

Publisher: Springer

ISBN: 3319084070

Page: 261

View: 765

This book constitutes the thoroughly refereed post-conference proceedings of the Second International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2013, held in conjunction with ECML/PKDD 2013 in Prague, Czech Republic, in September 2013. The 16 revised full papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on data streams and time series analysis, classification, clustering and pattern discovery, graphs, networks and relational data, machine learning and music data.

Intelligent Data Engineering and Automated Learning IDEAL 2012

Our results show an average improvement in computation time across the 26
data sets of approximately 14% and 18% for Kruskal-Wallis and Mood's Median
respectively. With a view to ... Buza, K.: Fusion methods for time-series
classification.

Intelligent Data Engineering and Automated Learning    IDEAL 2012

Author: Hujun Yin

Publisher: Springer

ISBN: 3642326390

Page: 862

View: 657

This book constitutes the refereed proceedings of the 13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012, held in Natal, Brazil, in August 2012. The 100 revised full papers presented were carefully reviewed and selected from more than 200 submissions for inclusion in the book and present the latest theoretical advances and real-world applications in computational intelligence.

Artificial Intelligence and Soft Computing

5 Conclusion In this paper, we proposed SUCCESS, a novel semi-supervised
time-series classifier. We discussed the relation between the minimal ... Buza,
K.A.: Fusion Methods for Time-Series Classification. Ph.D. thesis (2011) 4.
Cormen, T.

Artificial Intelligence and Soft Computing

Author: Leszek Rutkowski

Publisher: Springer

ISBN: 364238658X

Page: 637

View: 163

The two-volume set LNAI 7894 and LNCS 7895 constitutes the refereed proceedings of the 12th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2013, held in Zakopane, Poland in June 2013. The 112 revised full papers presented together with one invited paper were carefully reviewed and selected from 274 submissions. The 57 papers included in the first volume are organized in the following topical sections: neural networks and their applications; fuzzy systems and their applications; pattern classification; and computer vision, image and speech analysis.

Hybrid Artificial Intelligent Systems

Fusion of Similarity Measures for Time Series Classification Krisztian Buza,
Alexandros Nanopoulos, and Lars Schmidt-Thieme ... Recent results show that
the simple nearest neighbor method with an appropriate distance measure
performs ...

Hybrid Artificial Intelligent Systems

Author: Emilio Corchado

Publisher: Springer Science & Business Media

ISBN: 3642212212

Page: 490

View: 968

The two LNAI volumes 6678 and 6679 constitute the proceedings of the 6th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2011, held in Wroclaw, Poland, in May 2011. The 114 papers published in these proceedings were carefully reviewed and selected from 241 submissions. They are organized in topical sessions on hybrid intelligence systems on logistics and intelligent optimization; metaheuristics for combinatorial optimization and modelling complex systems; hybrid systems for context-based information fusion; methods of classifier fusion; intelligent systems for data mining and applications; systems, man, and cybernetics; hybrid artificial intelligence systems in management of production systems; habrid artificial intelligent systems for medical applications; and hybrid intelligent approaches in cooperative multi-robot systems.

Remote Sensing Time Series Image Processing

spatiotemporal data fusion methods are developed to fuse raw reflectance data (
Gao et al., 2006; Zhu et al., 2010). The synthetic reflectance data could then be
used for further applications, such as land cover classification and change ...

Remote Sensing Time Series Image Processing

Author: Qihao Weng

Publisher: CRC Press

ISBN: 1351680560

Page: 243

View: 244

Today, remote sensing technology is an essential tool for understanding the Earth and managing human-Earth interactions. There is a rapidly growing need for remote sensing and Earth observation technology that enables monitoring of world’s natural resources and environments, managing exposure to natural and man-made risks and more frequently occurring disasters, and helping the sustainability and productivity of natural and human ecosystems. The improvement in temporal resolution/revisit allows for the large accumulation of images for a specific location, creating a possibility for time series image analysis and eventual real-time assessments of scene dynamics. As an authoritative text, Remote Sensing Time Series Image Processing brings together active and recognized authors in the field of time series image analysis and presents to the readers the current state of knowledge and its future directions. Divided into three parts, the first addresses methods and techniques for generating time series image datasets. In particular, it provides guidance on the selection of cloud and cloud shadow detection algorithms for various applications. Part II examines feature development and information extraction methods for time series imagery. It presents some key remote sensing-based metrics, and their major applications in ecosystems and climate change studies. Part III illustrates various applications of time series image processing in land cover change, disturbance attribution, vegetation dynamics, and urbanization. This book is intended for researchers, practitioners, and students in both remote sensing and imaging science. It can be used as a textbook by undergraduate and graduate students majoring in remote sensing, imaging science, civil and electrical engineering, geography, geosciences, planning, environmental science, land use, energy, and GIS, and as a reference book by practitioners and professionals in the government, commercial, and industrial sectors.

Dissertation Abstracts International

As a specific application , the classification of time series data is chosen for the
research reported in this thesis . ... MIR - AEGA involves some novel
representation methods that proved to be effective for time series data . ...
Computational intelligence based classifier fusion models for biomedical
classification applications .

Dissertation Abstracts International

Author:

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Fuzzy Information Engineering

See Freehand Drawing System , Fuzzy ( FFDS ) pixel - based methods for the
segmentation of medical images . ... 407 - 8 POSSINFER and , 411 - 14
reasoning in , 409 possibilistic noninteractivity , 407 , 409 possibilistic resolution ,
419 possibilistic time - series model ... See expert knowledge , possiblity theory
and multisources information - fusion method for satellite image classification and
, 111 - 13 ...

Fuzzy Information Engineering

Author: Didier Dubois

Publisher: John Wiley & Sons Incorporated

ISBN:

Page: 712

View: 794

Fuzzy logic allows computer programmers to interpret ambiguous commands that ordinary, rigid programs are unable to decipher. For instance, computers can work with words like "tall" and "expensive" rather than 6'5" or $669.95. This book covers the use of fuzzy logic in the information science and information engineering fields.

Information Fusion in Data Mining

This book presents some recent fusion techniques that are currently in use in data mining, as well as data mining applications that use information fusion.

Information Fusion in Data Mining

Author: Prof. Vicenç Torra

Publisher: Springer

ISBN: 3540365192

Page: 234

View: 406

Information fusion is becoming a major requirement in data mining and knowledge discovery in databases. This book presents some recent fusion techniques that are currently in use in data mining, as well as data mining applications that use information fusion. Special focus of the book is on information fusion in preprocessing, model building and information extraction with various applications.

International Aerospace Abstracts

... and noise 63 p1182 A99-20402 Efficient multisensor fusion using
multidimensional assignment for multitarget tracking 63 p1182 ... fusions 43
p2072 A99-30285 Deriving long - term time series of sea ice cover from satellite
passive - microwave multisensor data sets 48 ... and acceleration sensor 35 p150
A99-11173 Methods for multisensor classification of airbome targets integrating
evidence theory ...

International Aerospace Abstracts

Author:

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Parametric and Nonparametric Approaches for Multisensor Data Fusion

2.2.1 Classification by Types of Sensor Data Sensor fusion may be performed on
time series , redundant , and / or complementary sensor data . Time series fusion
, by far the most common , allows for filtering of noisy sensor data and is
commonly used in target tracking ... Despite the obvious pixel - based
representation of these signals , the mathematical techniques used to process
and fuse these data ...

Parametric and Nonparametric Approaches for Multisensor Data Fusion

Author: Bing Ma

Publisher:

ISBN:

Page:

View: 268

Intelligent Data Engineering and Automated Learning IDEAL 2019

This two-volume set of LNCS 11871 and 11872 constitutes the thoroughly refereed conference proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, held in Manchester, UK, in ...

Intelligent Data Engineering and Automated Learning     IDEAL 2019

Author: Hujun Yin

Publisher: Springer Nature

ISBN: 3030336077

Page: 554

View: 129

This two-volume set of LNCS 11871 and 11872 constitutes the thoroughly refereed conference proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, held in Manchester, UK, in November 2019. The 94 full papers presented were carefully reviewed and selected from 149 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2019 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models (including neural networks, evolutionary computation and swarm intelligence), agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI.

Artificial Neural Networks

Thanks to specific learning strategies , prototypes and classes are created ,
adapted or eliminated in order to incorporate new knowledge from on - line data .
To do that , new learning rules have been developed into three stages : “
Classification ” , “ Fusion ” and ... In many other applications such as time series
classification , where the classes evolve faster , it will be a real benefit to use
dynamical ...

Artificial Neural Networks

Author:

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IEEE ISPRS Joint Workshop on Remote Sensing and Data Fusion Over Urban Areas

Detection of heat anomalies includes : • classification of regions in images by
brightness temperatures ... anomalous sources of thermal radiation in the given
image ; • determination of heat anomalies on the basis of time series with due ...
Depending on the noise conditions time of the day and nature and climate
situation , different standard techniques ... an example of detection of a heat
anomaly 183 IEEE / ISPRS Joint Workshop on Remote Sensing and Data Fusion
over Urban Areas.

IEEE ISPRS Joint Workshop on Remote Sensing and Data Fusion Over Urban Areas

Author:

Publisher: Institute of Electrical & Electronics Engineers(IEEE)

ISBN:

Page: 345

View: 420

This volume originates in the IEEE/ISPRS Workshop on Remote Sensing and Data Fusion, and examines power generation. It covers such topics as: 2D detection and classification; 3D urban modelling and reconstruction; data fusion over urban areas; and urban remote sensing applications.

Multiple Classifier Systems

This book constitutes the refereed proceedings of the First International Workshop on Multiple Classifier Systems, MCS 2000, held in Cagliari, Italy in June 2000.The 33 revised full papers presented together with five invited papers were ...

Multiple Classifier Systems

Author: Josef Kittler

Publisher: Springer

ISBN: 3540450149

Page: 408

View: 234

This book constitutes the refereed proceedings of the First International Workshop on Multiple Classifier Systems, MCS 2000, held in Cagliari, Italy in June 2000.The 33 revised full papers presented together with five invited papers were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on theoretical issues, multiple classifier fusion, bagging and boosting, design of multiple classifier systems, applications of multiple classifier systems, document analysis, and miscellaneous applications.

IGARSS 98

This is obvious advantage over most GIS data layers to be fused in land surface
classification for statistical methods ... one of the most unsupervised classification
in multisource remote sensing . challenging fusion problems is time series ...

IGARSS  98

Author: Tammy I. Stein

Publisher: Institute of Electrical & Electronics Engineers(IEEE)

ISBN:

Page: 2754

View: 571

The theme of the GRSS '98 emphasizes the role of remote sensing for managing limited natural resources. It covers topics such as: applications of remote sensing; electromagnetic problems; data processing techniques; geophysical models; and techniques and instrumentation.

Multiple Classifier Systems

Dietrich , C . , Schwenker , F . , Palm , G . : Classification of time series utilizing
temporal and decision fusion . ... Ghosh , J . , Beck , S . , Chu , C . C . : Evidence
combination techniques for robust classification of short - duration oceanic
signals .

Multiple Classifier Systems

Author:

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ISBN:

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View: 905

Signal Processing Sensor Fusion and Target Recognition

Transfer Function Models ( or Convolution models ) : These techniques model
the process using a transfer function and vary in the ... Stochastic Realization
Algorithm ( SRA ) for multiple time series modeling and spectral estimation using
state space models . ... Application to Image Enhancement Air - to - ground
surveillance of targets using Doppler radar for the classification of tanks and
trucks for UAV ...

Signal Processing  Sensor Fusion  and Target Recognition

Author:

Publisher:

ISBN:

Page:

View: 964