Data Complexity in Pattern Recognition

Machines capable of automatic pattern recognition have many fascinating uses in science and engineering as well as in our daily lives. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability. Tremendous progre...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Basu, Mitra (Επιμελητής έκδοσης), Ho, Tin Kam (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: London : Springer London, 2006.
Σειρά:Advanced Information and Knowledge Processing
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
LEADER 03919nam a22005055i 4500
001 978-1-84628-172-3
003 DE-He213
005 20151204145953.0
007 cr nn 008mamaa
008 100301s2006 xxk| s |||| 0|eng d
020 |a 9781846281723  |9 978-1-84628-172-3 
024 7 |a 10.1007/978-1-84628-172-3  |2 doi 
040 |d GrThAP 
050 4 |a Q337.5 
050 4 |a TK7882.P3 
072 7 |a UYQP  |2 bicssc 
072 7 |a COM016000  |2 bisacsh 
082 0 4 |a 006.4  |2 23 
245 1 0 |a Data Complexity in Pattern Recognition  |h [electronic resource] /  |c edited by Mitra Basu, Tin Kam Ho. 
264 1 |a London :  |b Springer London,  |c 2006. 
300 |a XVI, 300 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a Advanced Information and Knowledge Processing 
505 0 |a Theory and Methodology -- Measures of Geometrical Complexity in Classification Problems -- Object Representation, Sample Size, and Data Set Complexity -- Measures of Data and Classifier Complexity and the Training Sample Size -- Linear Separability in Descent Procedures for Linear Classifiers -- Data Complexity, Margin-Based Learning, and Popper’s Philosophy of Inductive Learning -- Data Complexity and Evolutionary Learning -- Classifier Domains of Competence in Data Complexity Space -- Data Complexity Issues in Grammatical Inference -- Applications -- Simple Statistics for Complex Feature Spaces -- Polynomial Time Complexity Graph Distance Computation for Web Content Mining -- Data Complexity in Clustering Analysis of Gene Microarray Expression Profiles -- Complexity of Magnetic Resonance Spectrum Classification -- Data Complexity in Tropical Cyclone Positioning and Classification -- Human-Computer Interaction for Complex Pattern Recognition Problems -- Complex Image Recognition and Web Security. 
520 |a Machines capable of automatic pattern recognition have many fascinating uses in science and engineering as well as in our daily lives. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability. Tremendous progress has been made in refining such algorithms; yet, automatic learning in many simple tasks in daily life still appears to be far from reach. This book takes a close view of data complexity and its role in shaping the theories and techniques in different disciplines and asks: • What is missing from current classification techniques? • When the automatic classifiers are not perfect, is it a deficiency of the algorithms by design, or is it a difficulty intrinsic to the classification task? • How do we know whether we have exploited to the fullest extent the knowledge embedded in the training data? Data Complexity in Pattern Recognition is unique in its comprehensive coverage and multidisciplinary approach from various methodological and practical perspectives. Researchers and practitioners alike will find this book an insightful reference to learn about the current status of available techniques as well as application areas. 
650 0 |a Computer science. 
650 0 |a Algorithms. 
650 0 |a Artificial intelligence. 
650 0 |a Pattern recognition. 
650 1 4 |a Computer Science. 
650 2 4 |a Pattern Recognition. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
650 2 4 |a Algorithm Analysis and Problem Complexity. 
700 1 |a Basu, Mitra.  |e editor. 
700 1 |a Ho, Tin Kam.  |e editor. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9781846281716 
830 0 |a Advanced Information and Knowledge Processing 
856 4 0 |u http://dx.doi.org/10.1007/978-1-84628-172-3  |z Full Text via HEAL-Link 
912 |a ZDB-2-SCS 
950 |a Computer Science (Springer-11645)