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Advantages and Pitfalls of Pattern Recognition

Advantages and Pitfalls of Pattern Recognition
  • Author : Horst Langer
  • Publsiher : Elsevier
  • Release : 23 November 2019
  • ISBN : 0128118431
  • Pages : 350 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKAdvantages and Pitfalls of Pattern Recognition

Summary:
Advantages and Pitfalls of Pattern Recognition presents various methods of pattern recognition and classification, useful to geophysicists, geochemists, geologists, geographers, data analysts, and educators and students of geosciences. Scientific and technological progress has dramatically improved the knowledge of our planet with huge amounts of digital data available in various fields of Earth Sciences, such as geology, geophysics, and geography. This has led to a new perspective of data analysis, requiring specific techniques that take several features into consideration rather than single parameters. Pattern recognition techniques offer a suitable key for processing and extracting useful information from the data of multivariate analysis. This book explores both supervised and unsupervised pattern recognition techniques, while providing insight into their application. Offers real-world examples of techniques for pattern recognition and handling multivariate data Includes examples, applications, and diagrams to enhance understanding Provides an introduction and access to relevant software packages


Advantages and Pitfalls of Pattern Recognition

Advantages and Pitfalls of Pattern Recognition
  • Author : Horst Langer,Susanna Falsaperla,Conny Hammer
  • Publisher : Elsevier
  • Release : 23 November 2019
GET THIS BOOKAdvantages and Pitfalls of Pattern Recognition

Advantages and Pitfalls of Pattern Recognition presents various methods of pattern recognition and classification, useful to geophysicists, geochemists, geologists, geographers, data analysts, and educators and students of geosciences. Scientific and technological progress has dramatically improved the knowledge of our planet with huge amounts of digital data available in various fields of Earth Sciences, such as geology, geophysics, and geography. This has led to a new perspective of data analysis, requiring specific techniques that take several features into consideration rather than



Artificial Neural Networks in Pattern Recognition

Artificial Neural Networks in Pattern Recognition
  • Author : Lionel Prevost,Simone Marinai,Friedhelm Schwenker
  • Publisher : Springer Science & Business Media
  • Release : 25 June 2008
GET THIS BOOKArtificial Neural Networks in Pattern Recognition

This book constitutes the refereed proceedings of the Third TC3 IAPR Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2008, held in Paris, France, in July 2008. The 18 revised full papers and 11 revised poster papers presented were carefully reviewed and selected from 57 submissions. The papers combine many ideas from machine learning, advanced statistics, signal and image processing for solving complex real-world pattern recognition problems. The papers are organized in topical sections on unsupervised learning, supervised learning, multiple classifiers, applications, and feature


Pattern Recognition

Pattern Recognition
  • Author : Shutao Li,Chenglin Liu,Yaonan Wang
  • Publisher : Springer
  • Release : 05 November 2014
GET THIS BOOKPattern Recognition

The two-volume set CCIS 483 and CCIS 484 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition, CCPR 2014, held in Changsha, China, in November 2014. The 112 revised full papers presented in two volumes were carefully reviewed and selected from 225 submissions. The papers are organized in topical sections on fundamentals of pattern recognition; feature extraction and classification; computer vision; image processing and analysis; video processing and analysis; biometric and action recognition; biomedical image analysis; document and speech analysis; pattern recognition applications.



Graph-Based Representations in Pattern Recognition

Graph-Based Representations in Pattern Recognition
  • Author : Luc Brun
  • Publisher : Springer Science & Business Media
  • Release : 23 March 2005
GET THIS BOOKGraph-Based Representations in Pattern Recognition

This book constitutes the refereed proceedings of the 5th IAPR International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2005, held in Poitiers, France in April 2005. The 18 revised full papers and 17 revised poster papers presented were carefully reviewed and selected from 50 submissions. The papers are organized in topical sections on graph representations, graphs and linear representations, combinatorial maps, matching, hierarchical graph abstraction and matching, inexact



Pattern Recognition Algorithms for Data Mining

Pattern Recognition Algorithms for Data Mining
  • Author : Sankar K. Pal,Pabitra Mitra
  • Publisher : CRC Press
  • Release : 27 May 2004
GET THIS BOOKPattern Recognition Algorithms for Data Mining

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks. Organized into eight chapters, the book


Pattern Recognition

Pattern Recognition
  • Author : R‚jean Plamondon,Heng-Da Cheng
  • Publisher : World Scientific
  • Release : 08 March 1991
GET THIS BOOKPattern Recognition

This book contains 15 reviewed papers selected from among those presented at the 4th Vision Interface Conference in Halifax, Canada 14 - 18 May 1990. The papers are grouped into three sections which deal with parallel architectures and neural networks, algorithms for analysis and processing, and systems and applications.



Similarity-Based Pattern Recognition

Similarity-Based Pattern Recognition
  • Author : Edwin Hancock,Marcello Pelillo
  • Publisher : Springer
  • Release : 28 June 2013
GET THIS BOOKSimilarity-Based Pattern Recognition

This book constitutes the proceedings of the Second International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD 2013, which was held in York, UK, in July 2013. The 18 papers presented were carefully reviewed and selected from 33 submissions. They cover a wide range of problems and perspectives, from supervised to unsupervised learning, from generative to discriminative models, from theoretical issues to real-world practical applications, and offer a timely picture of the state of the art in the field.


Progress in Pattern Recognition, Image Analysis and Applications

Progress in Pattern Recognition, Image Analysis and Applications
  • Author : José Francisco Martínez-Trinidad,Jesús Ariel Carrasco Ochoa
  • Publisher : Springer Science & Business Media
  • Release : 12 October 2006
GET THIS BOOKProgress in Pattern Recognition, Image Analysis and Applications

This book constitutes the refereed proceedings of the 11th Iberoamerican Congress on Pattern Recognition, CIARP 2006, held in Cancun, Mexico in November 2006. The 99 revised full papers presented together with three keynote articles were carefully reviewed and selected from 239 submissions. The papers cover ongoing research and mathematical methods.


Quality Recognition & Prediction

Quality Recognition & Prediction
  • Author : Shoichi Teshima
  • Publisher : Momentum Press
  • Release : 15 June 2012
GET THIS BOOKQuality Recognition & Prediction

The Mahalanobis-Taguchi data handling and pattern recognition system is widely established-- built and extended from the original quality control precepts of Genichi Taguchi. But the MT system is not always well understood. This new book makes the system much more vivid and concrete with real-life applications in a wide variety of disciplines from industry to general commerce. The book offers a clear computational method to show the user how to actually apply the system to real manufacturing control problems. With


Thinning Methodologies for Pattern Recognition

Thinning Methodologies for Pattern Recognition
  • Author : Ching Y. Suen,Patrick Shen-pei Wang
  • Publisher : World Scientific
  • Release : 08 March 1994
GET THIS BOOKThinning Methodologies for Pattern Recognition

Thinning is a technique widely used in the pre-processing stage of a pattern recognition system to compress data and to enhance feature extraction in the subsequent stage. It reduces a digitized pattern to a skeleton so that all resulting branches are 1 pixel thick. The method seems easy at first and has many advantages, however after two decades of intensive research, it has been found to be very challenging due to the difficulties in programming computers to do it.This collection


Pattern Recognition and Image Analysis

Pattern Recognition and Image Analysis
  • Author : IbPRIA 2003
  • Publisher : Springer Science & Business Media
  • Release : 22 May 2003
GET THIS BOOKPattern Recognition and Image Analysis

The refereed proceedings of the First Iberial Conference on Pattern Recognition and Image Analysis, IbPria 2003, held in Puerto de Andratx, Mallorca, Spain in June 2003. The 130 revised papers presented were carefully reviewed and selected from 185 full papers submitted. All current aspects of ongoing research in computer vision, image processing, pattern recognition, and speech recognition are addressed.