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Intelligent Data Engineering and Automated Learning -- IDEAL 2010: 11th International Conference, Paisley, UK, September 1-3, 2010, Proceedings de Colin Fyfe

Intelligent Data Engineering and Automated Learning -- IDEAL 2010: 11th International Conference, Paisley, UK, September 1-3, 2010, Proceedings: Lecture Notes in Computer Science, cartea 6283

Editat de Colin Fyfe, Peter Tino, Darryl Charles, Cesar Garcia Osorio, Hujun Yin
en Limba Engleză Paperback – 19 aug 2010

Considerăm că volumul Intelligent Data Engineering and Automated Learning -- IDEAL 2010, editat de o echipă coordonată de Colin Fyfe, reprezintă mai mult decât o simplă arhivă a unei conferințe; este o radiografie tehnică a stadiului inteligenței artificiale la începutul deceniului trecut. Ceea ce diferențiază acest volum de documentația tehnică standard este abordarea interdisciplinară care forțează limitele modelelor bio-inspirate și ale sistemelor hibride. Ne-a atras atenția în mod deosebit modul în care conceptele teoretice sunt imediat ancorate în aplicații de inginerie a datelor, oferind soluții pentru probleme complexe de regresie și clasificare.

Structura volumului reflectă o progresie logică de la algoritmi fundamentali la implementări specializate. Cuprinsul relevă o diversitate metodologică impresionantă: de la selecția instanțelor la scară largă prin algoritmi paraleli, până la utilizarea rețelelor neuronale de tip bottleneck pentru reducerea dimensiunilor. Această rigoare în organizarea materialului permite cercetătorului să navigheze rapid între tehnici de optimizare, precum algoritmii genetici aplicați în sisteme de irigații, și metode de procesare a limbajului natural pentru generarea de propoziții.

Poziționarea lucrării în contextul operei editorului principal este evidentă. Colin Fyfe aduce aici ecouri ale cercetărilor sale anterioare despre rețelele cu feedback negativ și adaptarea parametrilor pentru analiza exploratorie a datelor. Dacă în lucrări precum Hebbian Learning and Negative Feedback Networks autorul se concentra pe arhitecturi specifice, în Intelligent Data Engineering and Automated Learning -- IDEAL 2010 vedem o extindere a acestor principii către sisteme multi-agent și minerit de date aplicat în bioinformatică și analiza video. Este un volum dens, de 414 pagini, publicat de Springer în seria Lecture Notes in Computer Science, care păstrează standardul ridicat de noutate și calitate științifică.

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Specificații

ISBN-13: 9783642153808
ISBN-10: 3642153801
Pagini: 414
Ilustrații: XVI, 398 p. 134 illus.
Greutate: 0.64 kg
Ediția:2010
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Information Systems and Applications, incl. Internet/Web, and HCI

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

De ce să citești această carte

Recomandăm acest volum cercetătorilor și specialiștilor în informatică interesați de aplicații practice ale inteligenței artificiale. Cititorul câștigă acces la studii de caz concrete, precum detectarea cancerului prin metode kernel sau analiza seriilor temporale în imagistica satelitară. Este o resursă tehnică esențială pentru cei care doresc să înțeleagă evoluția algoritmilor de învățare automată și implementarea lor în sisteme distribuite complexe.

Descriere scurtă

The IDEAL conference has become a unique, established and broad interdisciplinary forum for experts, researchers and practitioners in many fields to interact with each other and with leading academics and industries in the areas of machine learning, information processing, data mining, knowledge management, bio-informatics, neu- informatics, bio-inspired models, agents and distributed systems, and hybrid systems. This volume contains the papers presented at the 11th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2010), which was held September 1–3, 2010 in the University of the West of Scotland, on its Paisley campus, 15 kilometres from the city of Glasgow, Scotland. All submissions were strictly pe- reviewed by the Programme Committee and only the papers judged with sufficient quality and novelty were accepted and included in the proceedings. The IDEAL conferences continue to evolve and this year’s conference was no exc- tion. The conference papers cover a wide variety of topics which can be classified by technique, aim or application. The techniques include evolutionary algorithms, artificial neural networks, association rules, probabilistic modelling, agent modelling, particle swarm optimization and kernel methods. The aims include regression, classification, clustering and generic data mining. The applications include biological information processing, text processing, physical systems control, video analysis and time series analysis.

Cuprins

Large Scale Instance Selection by Means of a Parallel Algorithm.- Typed Linear Chain Conditional Random Fields and Their Application to Intrusion Detection.- Generalized Derivative Based Kernelized Learning Vector Quantization.- Cost Optimization of a Localized Irrigation System Using Genetic Algorithms.- Dimension Reduction for Regression with Bottleneck Neural Networks.- Analysing Satellite Image Time Series by Means of Pattern Mining.- Sentences Generation by Frequent Parsing Patterns.- Gallbladder Boundary Segmentation from Ultrasound Images Using Active Contour Model.- On the Power of Topological Kernel in Microarray-Based Detection of Cancer.- An Evolutionary Multi-objective Optimization of Market Structures Using PBIL.- New Application of Graph Mining to Video Analysis.- Classification by Multiple Reducts-kNN with Confidence.- Towards Automatic Classification of Wikipedia Content.- Investigating the Behaviour of Radial Basis Function Networks in Regression and Classification of Geospatial Data.- A Comparison of Three Voting Methods for Bagging with the MLEM2 Algorithm.- Simplified Self-Adapting Skip Lists.- Multi-Agent Architecture with Support to Quality of Service and Quality of Control.- Robust 1-Norm Soft Margin Smooth Support Vector Machine.- A Generalization of Independence in Naive Bayes Model.- Interval Filter: A Locality-Aware Alternative to Bloom Filters for Hardware Membership Queries by Interval Classification.- Histogram Distance for Similarity Search in Large Time Series Database.- The Penalty Avoiding Rational Policy Making Algorithm in Continuous Action Spaces.- Applying Clustering Techniques to Reduce Complexity in Automated Planning Domains.- The M-OLAP Cube Selection Problem: A Hyper-polymorphic Algorithm Approach.- Privacy Preserving Techniquefor Euclidean Distance Based Mining Algorithms Using a Wavelet Related Transform.- Extracting Features from an Electrical Signal of a Non-Intrusive Load Monitoring System.- Annotation and Retrieval of Cell Images.- Adaptive Particle Swarm Optimizer for Feature Selection.- A Randomized Sphere Cover Classifier.- Directed Figure Codes with Weak Equality.- Surrogate Model for Continuous and Discrete Genetic Optimization Based on RBF Networks.- Relevance of Contextual Information in Compression-Based Text Clustering.- Simple Deterministically Constructed Recurrent Neural Networks.- Non-negative Matrix Factorization Implementation Using Graphic Processing Units.- A Neighborhood-Based Clustering by Means of the Triangle Inequality.- Selection of Structures with Grid Optimization, in Multiagent Data Warehouse.- Approximating the Covariance Matrix of GMMs with Low-Rank Perturbations.- Learning Negotiation Policies Using IB3 and Bayesian Networks.- Trajectory Based Behavior Analysis for User Verification.- Discovering Concept Mappings by Similarity Propagation among Substructures.- Clustering and Visualizing SOM Results.- A Hybrid Evolutionary Algorithm to Quadratic Three-Dimensional Assignment Problem with Local Search for Many-Core Graphics Processors.- Evolution Strategies for Objective Functions with Locally Correlated Variables.- Neural Data Analysis and Reduction Using Improved Framework of Information-Preserving EMD.- Improving the Performance of the Truncated Fourier Series Least Squares (TFSLS)Power System Load Model Using an Artificial Neural Network Paradigm.- An Efficient Approach to Clustering Real-Estate Listings.- Incremental Update of Cyclic Association Rules.

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