Gelbukh / Galicia-Haro / Espinoza | Nature-Inspired Computation and Machine Learning | Buch | 978-3-319-13649-3 | sack.de

Buch, Englisch, Band 8857, 522 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 8248 g

Reihe: Lecture Notes in Computer Science

Gelbukh / Galicia-Haro / Espinoza

Nature-Inspired Computation and Machine Learning

13th Mexican International Conference on Artificial Intelligence, MICAI2014, Tuxtla Gutiérrez, Mexico, November 16-22, 2014. Proceedings, Part II
2014
ISBN: 978-3-319-13649-3
Verlag: Springer International Publishing

13th Mexican International Conference on Artificial Intelligence, MICAI2014, Tuxtla Gutiérrez, Mexico, November 16-22, 2014. Proceedings, Part II

Buch, Englisch, Band 8857, 522 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 8248 g

Reihe: Lecture Notes in Computer Science

ISBN: 978-3-319-13649-3
Verlag: Springer International Publishing


The two-volume set LNAI 8856 and LNAI 8857 constitutes the proceedings of the 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, held in Tuxtla, Mexico, in November 2014. The total of 87 papers plus 1 invited talk presented in these proceedings were carefully reviewed and selected from 348 submissions. The first volume deals with advances in human-inspired computing and its applications. It contains 44 papers structured into seven sections: natural language processing, natural language processing applications, opinion mining, sentiment analysis, and social network applications, computer vision, image processing, logic, reasoning, and multi-agent systems, and intelligent tutoring systems.
The second volume deals with advances in nature-inspired computation and machine learning and contains also 44 papers structured into eight sections: genetic and evolutionary algorithms, neural networks, machine learning, machine learning applications to audio and text, data mining, fuzzy logic, robotics, planning, and scheduling, and biomedical applications.

Gelbukh / Galicia-Haro / Espinoza Nature-Inspired Computation and Machine Learning jetzt bestellen!

Zielgruppe


Research

Weitere Infos & Material


Genetic and Evolutionary Algorithms Performance Classification of Genetic Algorithms on Continuous Optimization Problems.- A Multi-objective Genetic Algorithm for the Software Project Scheduling Problem.- An Effective Method for MOGAs Initialization to Solve the Multi-Objective Next Release Problem.- Extension of the Method of Musical Composition for the Treatment of Multi-objective Optimization Problems.- k-Nearest-Neighbor by Differential Evolution for Time Series Forecasting.- A Binary Differential Evolution with Adaptive Parameters Applied to the Multiple Knapsack Problem.- Neural Networks.- The Best Neural Network Architecture.- On the Connection Weight Space Structure of a Two-Neuron Discrete Neural Network: Bifurcations of the Fixed Point at the Origin.- Stability of Modular Recurrent Trainable Neural Networks.- Intelligent Control of Induction Motor Based Comparative Study: Analysis of Two Topologies.- Auto-Adaptive Neuro-Fuzzy Parameter Regulator for Motor Drive.- Machine Learning.- Voting Algorithms Model with a Support Sets System by Class.- A Meta Classifier by Clustering of Classifiers.- Multiple Kernel Support Vector Machine Problem Is NP-Complete.- Feature Analysis for the Classification of Volcanic Seismic Events Using Support Vector Machines.- A Hierarchical Reinforcement Learning Based Artificial Intelligence for Non-Player Characters in Video Games.- Wind Power Forecasting Using Dynamic Bayesian Models.- Predictive Models Applied to Heavy Duty Equipment Management.- Customer Churn Prediction in Telecommunication Industry: With and without Counter-Example.- Machine Learning Applications to Audio and Text Audio-to-Audio Alignment for Performances Tracking.- Statistical Features Based Noise Type Identification.- Using Values of the Human Cochlea in the Macro and Micro Mechanical Model for Automatic Speech Recognition.- Identification of Vowel Sounds of the Choapan Variant of Zapotec Language.- An Open-Domain Cause-Effect Relation Detection from Paired Nominals.- An Alignment Comparator for Entity



Ihre Fragen, Wünsche oder Anmerkungen
Vorname*
Nachname*
Ihre E-Mail-Adresse*
Kundennr.
Ihre Nachricht*
Lediglich mit * gekennzeichnete Felder sind Pflichtfelder.
Wenn Sie die im Kontaktformular eingegebenen Daten durch Klick auf den nachfolgenden Button übersenden, erklären Sie sich damit einverstanden, dass wir Ihr Angaben für die Beantwortung Ihrer Anfrage verwenden. Selbstverständlich werden Ihre Daten vertraulich behandelt und nicht an Dritte weitergegeben. Sie können der Verwendung Ihrer Daten jederzeit widersprechen. Das Datenhandling bei Sack Fachmedien erklären wir Ihnen in unserer Datenschutzerklärung.