Title Advances in self-organizing maps, learning vector quantization, clustering and data visualization : proceedings of the 13th International Workshop, WSOM+ 2019, Barcelona, Spain, June 26-28, 2019 / editors, Alfredo Vellido, Karina Gibert, Cecilio Angulo and José David Martín Guerrero

Published Cham, Switzerland : Springer, [2020] ©2020

Click on the following:
Springer eBooks

Copies

Description 1 online resource : illustrations Series Advances in intelligent systems and computing, 2194-5357 ; volume 976 Advances in intelligent systems and computing ; v. 976. Contents Self-organizing Maps : Theoretical Developments -- Look and Feel What and How Recurrent Self-Organizing Maps Learn -- Self-Organizing Mappings on the Flag Manifold -- Self-Organizing Maps with Convolutional Layers -- Cellular Self-Organising Maps -- CSOM -- A Probabilistic Method for Pruning CADJ Graphs with Applications to SOM Clustering Practical Applications of Self-Organizing Maps, Learning Vector Quantization and Clustering -- SOM-Based Anomaly Detection and Localization for Space Subsystems -- Self-Organizing Maps in Earth Observation Data Cubes Analysis -- Competencies in Higher Education : A Feature Analysis with Self-Organizing Maps -- Using SOM-Based Visualization to Analyze the Financial Performance of Consumer Discretionary Firms -- Novelty Detection with Self-Organizing Maps for Autonomous Extraction of Salient Tracking Features -- Robust Adaptive SOMs Challenges in a Varied Datasets Analytics -- Detection of Abnormal Flights Using Fickle Instances in SOM Maps -- LVQ-type Classifiers for Condition Monitoring of Induction Motors : A Performance Comparison -- When Clustering the Multiscalar Fingerprint of the City Reveals Its Segregation Patterns -- Using Hierarchical Clustering to Understand Behavior of 3D Printer Sensors -- A Walk Through Spectral Bands : Using Virtual Reality to Better Visualize Hyperspectral Data -- Incremental Traversability Assessment Learning Using Growing Neural Gas Algorithm -- Learning Vector Quantization : Theoretical Developments -- Investigation of Activation Functions for Generalized Learning Vector Quantization -- Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks -- Passive Concept Drift Handling via Momentum Based Robust Soft Learning Vector Quantization -- Prototype-Based Classifiers in the Presence of Concept Drift : A Modelling Framework -- Theoretical Developments in Clustering, Deep Learning and Neural Gas -- Soft Subspace Topological Clustering over Evolving Data Stream -- Solving a Tool-Based Interaction Task Using Deep Reinforcement Learning with Visual Attention -- Approximate Linear Dependence as a Design Method for Kernel Prototype-Based Classifiers -- Subspace Quantization on the Grassmannian -- Variants of Fuzzy Neural Gas -- Autoencoders Covering Space as a Life-Long Classifier -- Life Science Applications -- Progressive Clustering and Characterization of Increasingly Higher Dimensional Datasets with Living Self-organizing Maps -- A Voting Ensemble Method to Assist the Diagnosis of Prostate Cancer Using Multiparametric MRI -- Classifying and Grouping Mammography Images into Communities Using Fisher Information Networks to Assist the Diagnosis of Breast Cancer -- Network Community Cluster-Based Analysis for the Identification of Potential Leukemia Drug Targets -- Searching for the Origins of Life -- Detecting RNA Life Signatures Using Learning Vector Quantization -- Simultaneous Display of Front and Back Sides of Spherical SOM for Health Data Analysis Summary This book gathers papers presented at the 13th International Workshop on Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization (WSOM+), which was held in Barcelona, Spain, from the 26th to the 28th of June 2019. Since being founded in 1997, the conference has showcased the state of the art in unsupervised machine learning methods related to the successful and widely used self-organizing map (SOM) method, and extending its scope to clustering and data visualization. In this installment of the AISC series, the reader will find theoretical research on SOM, LVQ and related methods, as well as numerous applications to problems in fields ranging from business and engineering to the life sciences. Given the scope of its coverage, the book will be of interest to machine learning researchers and practitioners in general and, more specifically, to those looking for the latest developments in unsupervised learning and data visualization Bibliography Includes bibliographical references and index Notes Online resource; title from PDF title page (EBSCO, viewed April 30, 2019) Subject Neural networks (Computer science) -- Congresses Self-organizing maps -- Congresses Self-organizing systems -- Congresses COMPUTERS -- General. Neural networks (Computer science) Self-organizing maps Self-organizing systems Genre/Form proceedings (reports) Conference papers and proceedings Conference papers and proceedings. Actes de congrès. Form Electronic book Author Vellido, Alfredo, editor. Gibert, Karina, editor Angulo, Cecilio, editor. Martín Guerrero, José David, editor. ISBN 9783030196424 3030196429 3030196410 9783030196417 9783030196431 3030196437

Other Titles WSOM+ 2019

  Permalink