Cantitate/Preț
Produs
Total produse
Vezi coșul
Machine Learning and Data Mining in Pattern Recognition: 6th International Conference, MLDM 2009, Leipzig, Germany, July 23-25, 2009, Proceedings de Petra Perner

Machine Learning and Data Mining in Pattern Recognition: 6th International Conference, MLDM 2009, Leipzig, Germany, July 23-25, 2009, Proceedings: Lecture Notes in Computer Science, cartea 5632

Editat de Petra Perner
en Limba Engleză Paperback – 10 iul 2009
There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern r- ognition. Our thanks go to all those who took part in this year's MLDM. We appre- ate their submissions and the ideas shared with the Program Committee. We received over 205 submissions from all over the world to the International Conference on - chine Learning and Data Mining, MLDM 2009. The Program Committee carefully selected the best papers for this year’s program and gave detailed comments on each submitted paper. There were 63 papers selected for oral presentation and 17 papers for poster presentation. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data-mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining. Among these topics this year were special contributions to subtopics such as attribute discre- zation and data preparation, novelty and outlier detection, and distances and simila- ties.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (12) 32784 lei  6-8 săpt.
  Springer International Publishing – 13 iul 2015 32784 lei  6-8 săpt.
  Springer International Publishing – 8 iul 2018 32799 lei  6-8 săpt.
  Springer – 4 iul 2017 32956 lei  6-8 săpt.
  Springer – 4 aug 2014 33404 lei  6-8 săpt.
  Springer Berlin, Heidelberg – 9 iul 2013 33766 lei  6-8 săpt.
  Springer Berlin, Heidelberg – 12 aug 2011 33847 lei  6-8 săpt.
  Springer Berlin, Heidelberg – 7 iul 2012 34004 lei  6-8 săpt.
  Springer Berlin, Heidelberg – 16 iul 2001 34734 lei  38-44 zile
  Springer International Publishing – 8 iul 2018 46631 lei  6-8 săpt.
  Springer International Publishing – 28 iun 2016 64539 lei  6-8 săpt.
  Springer Berlin, Heidelberg – 10 iul 2009 64715 lei  6-8 săpt.
  Springer – 16 iul 2007 65420 lei  6-8 săpt.

Din seria Lecture Notes in Computer Science

Preț: 64715 lei

Preț vechi: 80893 lei
-20% Nou

Puncte Express: 971
Preț estimativ în valută:
11450€ • 13461$ • 10028£

Carte tipărită la comandă

Livrare economică 28 ianuarie-11 februarie 26

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783642030697
ISBN-10: 3642030696
Pagini: 840
Ilustrații: XIV, 824 p.
Dimensiuni: 155 x 235 x 44 mm
Greutate: 1.18 kg
Ediția:2009
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

Attribute Discretization and Data Preparation.- Improved Comprehensibility and Reliability of Explanations via Restricted Halfspace Discretization.- Selection of Subsets of Ordered Features in Machine Learning.- Combination of Vector Quantization and Visualization.- Discretization of Target Attributes for Subgroup Discovery.- Preserving Privacy in Time Series Data Classification by Discretization.- Using Resampling Techniques for Better Quality Discretization.- Classification.- A Large Margin Classifier with Additional Features.- Sequential EM for Unsupervised Adaptive Gaussian Mixture Model Based Classifier.- Optimal Double-Kernel Combination for Classification.- Efficient AdaBoost Region Classification.- A Linear Classification Method in a Very High Dimensional Space Using Distributed Representation.- PMCRI: A Parallel Modular Classification Rule Induction Framework.- Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method.- ODDboost: Incorporating Posterior Estimates into AdaBoost.- Ensemble Classifier Learning.- Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach.- Relevance and Redundancy Analysis for Ensemble Classifiers.- Drift-Aware Ensemble Regression.- Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees.- Association Rules and Pattern Mining.- Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies.- Pattern Mining with Natural Language Processing: An Exploratory Approach.- Is the Distance Compression Effect Overstated? Some Theory and Experimentation.- Support Vector Machines.- Fast Local Support Vector Machines for Large Datasets.- The Effect of Domain Knowledge on Rule Extraction from Support Vector Machines.- Towards B-Coloring of SOM.- Clustering.- CSBIterKmeans: A New Clustering Algorithm Based on Quantitative Assessment of the Clustering Quality.- Agent-Based Non-distributed and Distributed Clustering.- An Evidence Accumulation Approach to Constrained Clustering Combination.- Fast Spectral Clustering with Random Projection and Sampling.- How Much True Structure Has Been Discovered?.- Efficient Clustering of Web-Derived Data Sets.- A Probabilistic Approach for Constrained Clustering with Topological Map.- Novelty and Outlier Detection.- Relational Frequent Patterns Mining for Novelty Detection from Data Streams.- A Comparative Study of Outlier Detection Algorithms.- Outlier Detection with Explanation Facility.- Learning.- Concept Learning from (Very) Ambiguous Examples.- Finding Top-N Pseudo Formal Concepts with Core Intents.- On Fixed Convex Combinations of No-Regret Learners.- An Improved Tabu Search (ITS) Algorithm Based on Open Cover Theory for Global Extremums.- The Needles-in-Haystack Problem.- Data Mining on Multimedia Data.- An Evidence-Driven Probabilistic Inference Framework for Semantic Image Understanding.- Detection of Masses in Mammographic Images Using Simpson’s Diversity Index in Circular Regions and SVM.- Mining Lung Shape from X-Ray Images.- A Wavelet-Based Method for Detecting Seismic Anomalies in Remote Sensing Satellite Data.- Spectrum Steganalysis of WAV Audio Streams.- Audio-Based Emotion Recognition in Judicial Domain: A Multilayer Support Vector Machines Approach.- Learning with a Quadruped Chopstick Robot.- Dissimilarity Based Vector Space Embedding of Graphs Using Prototype Reduction Schemes.- Text Mining.- Using Graph-Kernels to Represent Semantic Information in Text Classification.- A General Framework of Feature Selection for Text Categorization.- New SemanticSimilarity Based Model for Text Clustering Using Extended Gloss Overlaps.- Aspects of Data Mining.- Learning Betting Tips from Users’ Bet Selections.- An Approach to Web-Scale Named-Entity Disambiguation.- A General Learning Method for Automatic Title Extraction from HTML Pages.- Regional Pattern Discovery in Geo-referenced Datasets Using PCA.- Memory-Based Modeling of Seasonality for Prediction of Climatic Time Series.- A Neural Approach for SME’s Credit Risk Analysis in Turkey.- Assisting Data Mining through Automated Planning.- Predictions with Confidence in Applications.- Data Mining in Medicine.- Aligning Bayesian Network Classifiers with Medical Contexts.- Assessing the Eligibility of Kidney Transplant Donors.- Lung Nodules Classification in CT Images Using Simpson’s Index, Geometrical Measures and One-Class SVM.

Textul de pe ultima copertă

This book constitutes the refereed proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2009, held in Leipzig, Germany, in July 2009.
The 63 revised full papers presented were carefully reviewed and selected from 205 submissions. The papers are organized in topical sections on attribute discretization and data preparation; classification; ensemble classifier learning; associate rules and pattern minig; support vector machines; clustering; novelty and outlier detection; learning; data mining and multimedia data; text mining; aspects of data mining; as well as data mining in medicine.

Caracteristici

Up-to-date results Fast track conference proceedings State-of-the-art report Includes supplementary material: sn.pub/extras