Cao Jiuwen (243 results)

Aufl. 2014. 235 mm x 155 mm. VIII, 400 p. Hardcover. Volume 2. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Proceedings in Adaptation, Learning and Optimization ; 4. Sprache: Englisch.

Condition: Very Good. 2015 Springer International (Cham, Switzerland), 6 1/2 x 9 1/2 inches tall pictorial hardcover, no dust jacket (as issued), illustrated with black-and-white photographs, charts and graphs, viii, 400 pp. Slight bumping to edges of covers. Otherwise, a very good to near fine copy - clean, bright and unmarked. Due to the weight of the book, additional postage will be required for standard international orders. ~SP05~ [2.5P] contains some selected papers from the International Conference on Extreme Learning Machine 2014, which was held in Singapore, December 8-10, 2014. This conference brought together the researchers and practitioners of Extreme Learning Machine (ELM) from a variety of fields to promote research and development of "learning without iterative tuning". The book covers theories, algorithms and applications of ELM. Contents: Using Extreme Learning Machine for Filamentous Bulking Prediction and Forecast in Wastewater Treatment Plants; Extreme Learning Machine for Linear Dynamical Systems Classification: Application to Human Activity Recognition; Lens Distortion Correction Using ELM; Pedestrian Detection in Thermal Infrared Image using Extreme Learning Machine; Dynamic Texture Video Classification Using Extreme Learning Machine; Uncertain XML Documents Classification Using Extreme Learning Machine; Encrypted traffic identification based on randomness sparse feature and extreme learning machine; Network Intrusion Detection Based on Extreme Learning Machine; A Study on Three-dimensional Motion History Image and Extreme Learning Machine Oriented Body Movements Trajectory Recognition; An Improved ELM Algorithm for the Measurement of Hot Metal Temperature in Blast Furnace; Wi-Fi and Motion Sensors based Indoor Localization Combining ELM and Particle Filter; Online Sequential Extreme Learning Machine for Watermarking; Adaptive neural control of quadrotor helicopter with extreme learning machine; Keyword Search on Probabilistic XML Data based on ELM; A Novel HVS Based Gray Scale Image Watermarking Scheme Using Fast Fuzzy; ELM Hybrid Architecture; Wearable EyeGlass based Fall Detection using Weighted ELM; Concise Feature Extraction based ELM for Active Service Quality Prediction; Multi-class AdaBoost ELM and Its Application in LBP Based Face Recognition; Detecting Copy Directions among Programs Using Extreme Learning Machines; Extreme learning machine for reservoir parameter estimation in heterogeneous reservoir; Multifault Diagnosis for Rolling Element Bearings Based on Extreme Learning Machine; Gradient-based No-Reference Image Blur Assessment Using Extreme Learning Machine; RFID Enabled Indoor Positioning for Real-time Manufacturing Execution System based on OS-ELM; An Online Sequential Extreme Learning Machine for Tidal Prediction based on Improved Gath-Geva Fuzzy Segmentation; Recognition of Human Stair Ascent and Descent Activities based on Extreme Learning Machine; ELM Based Dynamic Modeling for Online Prediction of Content in Molten Iron; Distributed Learning over Massive XML Documents in ELM Feature Space; Hyperspectral Image Nonlinear Unmixing by Ensemble ELM Regression; Text-Image Separation and Indexing in Historic Patent Document Image Based on Extreme Learning Machine; Anomaly Detection with ELM-based Visual Attribute and Spatio-temporal Pyramid; Modelling and Prediction of Surface Roughness and Power Consumption using Parallel Extreme Learning Machine based Particle Swarm Optimization; OS-ELM based Emotion Recognition for Empathetic Elderly Companion; Access Behavior Prediction in Distributed StorageSystem using Regularized Extreme Learning Machine; ELM Based Fast CFD Model with Sensor Adjustment; Melasma Image Segmentation Using Extreme Learning Machine; Detection of Drivers' Distraction Using Semi-Supervised Extreme Learning Machine; Driver Workload Detection in On-road Driving Environment using Machine Learning.

Hardcover. Condition: Sehr gut. 1. Auflage. Unread book in excellent condition. Language - English. Ships from Berlin.

Condition: Good. Most items will be dispatched the same or the next working day. A copy that has been read but remains in clean condition. All of the pages are intact and the cover is intact and the spine may show signs of wear. The book may have minor markings which are not specifically mentioned.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Condition: New. In.

Paperback. Condition: new. Paperback. This book contains some selected papers from the International Conference on Extreme Learning Machine 2019, which was held in Yangzhou, China, December 1416, 2019. Extreme Learning Machines (ELMs) aim to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental learning particles filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that random hidden neurons capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. The main theme of ELM2019 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning.This conference provides a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning.This book covers theories, algorithms and applications of ELM. It gives readers a glance of the most recent advances of ELM. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: As New. Unread book in perfect condition.

Condition: New. In.

Condition: New. In.

Condition: As New. Unread book in perfect condition.

Condition: New.

Paperback. Condition: new. Paperback. This book contains some selected papers from the International Conference on Extreme Learning Machine 2016, which was held in Singapore, December13-15, 2016. This conference will provide a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. Extreme Learning Machines (ELM) aims to break the barriers between the conventional artificial learning techniques and biological learning mechanism. ELM represents a suite of (machine or possibly biological) learning techniques in which hidden neurons need not be tuned. ELM learning theories show that very effective learning algorithms can be derived based on randomly generated hidden neurons (with almost any nonlinear piecewise activation functions), independent of training data and application environments. Increasingly, evidence from neurosciencesuggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that random hidden neurons capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. ELM offers significant advantages over conventional neural network learning algorithms such as fast learning speed, ease of implementation, and minimal need for human intervention. ELM also shows potential as a viable alternative technique for largescale computing and artificial intelligence.This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM. Extreme Learning Machines (ELM) aims to break the barriers between the conventional artificial learning techniques and biological learning mechanism. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condition: new. Hardcover. This book contains some selected papers from the International Conference on Extreme Learning Machine 2016, which was held in Singapore, December13-15, 2016. This conference will provide a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. Extreme Learning Machines (ELM) aims to break the barriers between the conventional artificial learning techniques and biological learning mechanism. ELM represents a suite of (machine or possibly biological) learning techniques in which hidden neurons need not be tuned. ELM learning theories show that very effective learning algorithms can be derived based on randomly generated hidden neurons (with almost any nonlinear piecewise activation functions), independent of training data and application environments. Increasingly, evidence from neurosciencesuggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that random hidden neurons capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. ELM offers significant advantages over conventional neural network learning algorithms such as fast learning speed, ease of implementation, and minimal need for human intervention. ELM also shows potential as a viable alternative technique for largescale computing and artificial intelligence.This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM. Extreme Learning Machines (ELM) aims to break the barriers between the conventional artificial learning techniques and biological learning mechanism. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: As New. Unread book in perfect condition.

Condition: New.

Gebunden. Condition: New.

Condition: As New. Unread book in perfect condition.

Hardcover. Condition: new. Hardcover. This book contains some selected papers from the International Conference on Extreme Learning Machine 2019, which was held in Yangzhou, China, December 1416, 2019. Extreme Learning Machines (ELMs) aim to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental learning particles filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that random hidden neurons capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. The main theme of ELM2019 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning.This conference provides a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning.This book covers theories, algorithms and applications of ELM. It gives readers a glance of the most recent advances of ELM. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. This book contains some selected papers from the International Conference on Extreme Learning Machine (ELM) 2017, held in Yantai, China, October 47, 2017. The book covers theories, algorithms and applications of ELM.Extreme Learning Machines (ELM) aims to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental `learning particles filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series,etc) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that random hidden neurons capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. This conference will provide a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. It gives readers a glance of the most recent advances of ELM. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series,etc) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Condition: New.