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Explainable AI: Interpreting, Explaining and Visualizing Deep Learning [[electronic resource] /] / edited by Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, Klaus-Robert Müller
| Explainable AI: Interpreting, Explaining and Visualizing Deep Learning [[electronic resource] /] / edited by Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, Klaus-Robert Müller |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XI, 439 p. 152 illus., 119 illus. in color.) |
| Disciplina | 006.32 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Optical data processing Computers Computer security Computer organization Artificial Intelligence Image Processing and Computer Vision Computing Milieux Systems and Data Security Computer Systems Organization and Communication Networks |
| ISBN | 3-030-28954-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Towards Explainable Artificial Intelligence -- Transparency: Motivations and Challenges -- Interpretability in Intelligent Systems: A New Concept? -- Understanding Neural Networks via Feature Visualization: A Survey -- Interpretable Text-to-Image Synthesis with Hierarchical Semantic Layout Generation -- Unsupervised Discrete Representation Learning -- Towards Reverse-Engineering Black-Box Neural Networks -- Explanations for Attributing Deep Neural Network Predictions -- Gradient-Based Attribution Methods -- Layer-Wise Relevance Propagation: An Overview -- Explaining and Interpreting LSTMs -- Comparing the Interpretability of Deep Networks via Network Dissection -- Gradient-Based vs. Propagation-Based Explanations: An Axiomatic Comparison -- The (Un)reliability of Saliency Methods -- Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation -- Understanding Patch-Based Learning of Video Data by Explaining Predictions -- Quantum-Chemical Insights from Interpretable Atomistic Neural Networks -- Interpretable Deep Learning in Drug Discovery -- Neural Hydrology: Interpreting LSTMs in Hydrology -- Feature Fallacy: Complications with Interpreting Linear Decoding Weights in fMRI -- Current Advances in Neural Decoding -- Software and Application Patterns for Explanation Methods. |
| Record Nr. | UNISA-996466320103316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning / / edited by Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, Klaus-Robert Müller
| Explainable AI: Interpreting, Explaining and Visualizing Deep Learning / / edited by Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, Klaus-Robert Müller |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XI, 439 p. 152 illus., 119 illus. in color.) |
| Disciplina |
006.32
006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Optical data processing Computers Computer security Computer organization Artificial Intelligence Image Processing and Computer Vision Computing Milieux Systems and Data Security Computer Systems Organization and Communication Networks |
| ISBN | 3-030-28954-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Towards Explainable Artificial Intelligence -- Transparency: Motivations and Challenges -- Interpretability in Intelligent Systems: A New Concept? -- Understanding Neural Networks via Feature Visualization: A Survey -- Interpretable Text-to-Image Synthesis with Hierarchical Semantic Layout Generation -- Unsupervised Discrete Representation Learning -- Towards Reverse-Engineering Black-Box Neural Networks -- Explanations for Attributing Deep Neural Network Predictions -- Gradient-Based Attribution Methods -- Layer-Wise Relevance Propagation: An Overview -- Explaining and Interpreting LSTMs -- Comparing the Interpretability of Deep Networks via Network Dissection -- Gradient-Based vs. Propagation-Based Explanations: An Axiomatic Comparison -- The (Un)reliability of Saliency Methods -- Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation -- Understanding Patch-Based Learning of Video Data by Explaining Predictions -- Quantum-Chemical Insights from Interpretable Atomistic Neural Networks -- Interpretable Deep Learning in Drug Discovery -- Neural Hydrology: Interpreting LSTMs in Hydrology -- Feature Fallacy: Complications with Interpreting Linear Decoding Weights in fMRI -- Current Advances in Neural Decoding -- Software and Application Patterns for Explanation Methods. |
| Record Nr. | UNINA-9910349299503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning Meets Quantum Physics [[electronic resource] /] / edited by Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller
| Machine Learning Meets Quantum Physics [[electronic resource] /] / edited by Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xvi, 467 pages) : illustrations |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Physics |
| Soggetto topico |
Quantum physics
Physics Machine learning Chemistry, Physical and theoretical Quantum Physics Numerical and Computational Physics, Simulation Machine Learning Theoretical and Computational Chemistry |
| ISBN | 3-030-40245-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to Material Modeling -- Kernel Methods for Quantum Chemistry -- Introduction to Neural Networks -- Building nonparametric n-body force fields using Gaussian process regression -- Machine-learning of atomic-scale properties based on physical principles -- Quantum Machine Learning with Response Operators in Chemical Compound Space -- Physical extrapolation of quantum observables by generalization with Gaussian Processes -- Message Passing Neural Networks -- Learning representations of molecules and materials with atomistic neural networks -- Molecular Dynamics with Neural Network Potentials -- High-Dimensional Neural Network Potentials for Atomistic Simulations -- Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights -- Active learning and Uncertainty Estimation -- Machine Learning for Molecular Dynamics on Long Timescales -- Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design -- Polymer Genome: A polymer informatics platform to accelerate polymer discovery -- Bayesian Optimization in Materials Science -- Recommender Systems for Materials Discovery -- Generative Models for Automatic Chemical Design. |
| Record Nr. | UNISA-996418435403316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning Meets Quantum Physics [[electronic resource] /] / edited by Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller
| Machine Learning Meets Quantum Physics [[electronic resource] /] / edited by Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xvi, 467 pages) : illustrations |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Physics |
| Soggetto topico |
Quantum physics
Physics Machine learning Chemistry, Physical and theoretical Quantum Physics Numerical and Computational Physics, Simulation Machine Learning Theoretical and Computational Chemistry |
| ISBN | 3-030-40245-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to Material Modeling -- Kernel Methods for Quantum Chemistry -- Introduction to Neural Networks -- Building nonparametric n-body force fields using Gaussian process regression -- Machine-learning of atomic-scale properties based on physical principles -- Quantum Machine Learning with Response Operators in Chemical Compound Space -- Physical extrapolation of quantum observables by generalization with Gaussian Processes -- Message Passing Neural Networks -- Learning representations of molecules and materials with atomistic neural networks -- Molecular Dynamics with Neural Network Potentials -- High-Dimensional Neural Network Potentials for Atomistic Simulations -- Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights -- Active learning and Uncertainty Estimation -- Machine Learning for Molecular Dynamics on Long Timescales -- Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design -- Polymer Genome: A polymer informatics platform to accelerate polymer discovery -- Bayesian Optimization in Materials Science -- Recommender Systems for Materials Discovery -- Generative Models for Automatic Chemical Design. |
| Record Nr. | UNISA-996462952503316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning Meets Quantum Physics [[electronic resource] /] / edited by Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller
| Machine Learning Meets Quantum Physics [[electronic resource] /] / edited by Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xvi, 467 pages) : illustrations |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Physics |
| Soggetto topico |
Quantum physics
Physics Machine learning Chemistry, Physical and theoretical Quantum Physics Numerical and Computational Physics, Simulation Machine Learning Theoretical and Computational Chemistry |
| ISBN | 3-030-40245-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to Material Modeling -- Kernel Methods for Quantum Chemistry -- Introduction to Neural Networks -- Building nonparametric n-body force fields using Gaussian process regression -- Machine-learning of atomic-scale properties based on physical principles -- Quantum Machine Learning with Response Operators in Chemical Compound Space -- Physical extrapolation of quantum observables by generalization with Gaussian Processes -- Message Passing Neural Networks -- Learning representations of molecules and materials with atomistic neural networks -- Molecular Dynamics with Neural Network Potentials -- High-Dimensional Neural Network Potentials for Atomistic Simulations -- Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights -- Active learning and Uncertainty Estimation -- Machine Learning for Molecular Dynamics on Long Timescales -- Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design -- Polymer Genome: A polymer informatics platform to accelerate polymer discovery -- Bayesian Optimization in Materials Science -- Recommender Systems for Materials Discovery -- Generative Models for Automatic Chemical Design. |
| Record Nr. | UNISA-996466798003316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning Meets Quantum Physics / / edited by Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller
| Machine Learning Meets Quantum Physics / / edited by Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xvi, 467 pages) : illustrations |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Physics |
| Soggetto topico |
Quantum physics
Physics Machine learning Chemistry, Physical and theoretical Quantum Physics Numerical and Computational Physics, Simulation Machine Learning Theoretical and Computational Chemistry |
| ISBN | 3-030-40245-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to Material Modeling -- Kernel Methods for Quantum Chemistry -- Introduction to Neural Networks -- Building nonparametric n-body force fields using Gaussian process regression -- Machine-learning of atomic-scale properties based on physical principles -- Quantum Machine Learning with Response Operators in Chemical Compound Space -- Physical extrapolation of quantum observables by generalization with Gaussian Processes -- Message Passing Neural Networks -- Learning representations of molecules and materials with atomistic neural networks -- Molecular Dynamics with Neural Network Potentials -- High-Dimensional Neural Network Potentials for Atomistic Simulations -- Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights -- Active learning and Uncertainty Estimation -- Machine Learning for Molecular Dynamics on Long Timescales -- Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design -- Polymer Genome: A polymer informatics platform to accelerate polymer discovery -- Bayesian Optimization in Materials Science -- Recommender Systems for Materials Discovery -- Generative Models for Automatic Chemical Design. |
| Record Nr. | UNINA-9910410002503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Neural Networks: Tricks of the Trade [[electronic resource] /] / edited by Grégoire Montavon, Geneviève Orr, Klaus-Robert Müller
| Neural Networks: Tricks of the Trade [[electronic resource] /] / edited by Grégoire Montavon, Geneviève Orr, Klaus-Robert Müller |
| Edizione | [2nd ed. 2012.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012 |
| Descrizione fisica | 1 online resource (XII, 769 p. 223 illus.) |
| Disciplina | 006.32 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Computer science
Artificial intelligence Algorithms Pattern recognition systems Dynamics Nonlinear theories Application software Theory of Computation Artificial Intelligence Automated Pattern Recognition Applied Dynamical Systems Computer and Information Systems Applications |
| ISBN | 3-642-35289-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Preface on Speeding Learning -- 1. Efficient BackProp -- Preface on Regularization Techniques to Improve Generalization -- 2. Early Stopping — But When? -- 3. A Simple Trick for Estimating the Weight Decay Parameter -- 4. Controlling the Hyperparameter Search in MacKay’s Bayesian Neural Network Framework.- 5. Adaptive Regularization in Neural Network Modeling -- 6. Large Ensemble Averaging -- Preface on Improving Network Models and Algorithmic Tricks -- 7. Square Unit Augmented, Radially Extended, Multilayer Perceptrons -- 8. A Dozen Tricks with Multitask Learning -- 9. Solving the Ill-Conditioning in Neural Network Learning -- 10. Centering Neural Network Gradient Factors -- 11. Avoiding Roundoff Error in Backpropagating Derivatives.- 12. Transformation Invariance in Pattern Recognition –Tangent Distance and Tangent Propagation -- 13. Combining Neural Networks and Context-Driven Search for On-line, Printed Handwriting Recognition in the Newtons -- 14. Neural Network Classification and Prior Class Probabilities -- 15. Applying Divide and Conquer to Large Scale Pattern Recognition Tasks -- Preface on Tricks for Time Series -- 16. Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions -- 17. How to Train Neural Networks -- Preface on Big Learning in Deep Neural Networks -- 18. Stochastic Gradient Descent Tricks.- 19. Practical Recommendations for Gradient-Based Training of Deep Architectures -- 20. Training Deep and Recurrent Networks with Hessian-Free Optimization -- 21. Implementing Neural Networks Efficiently -- Preface on Better Representations: Invariant, Disentangled and Reusable -- 22. Learning Feature Representations with K-Means -- 23. Deep Big Multilayer Perceptrons for Digit Recognition -- 24. A Practical Guide to Training Restricted Boltzmann Machines -- 25. Deep Boltzmann Machines and the Centering Trick -- 26. Deep Learning via Semi-supervised Embedding -- Preface on Identifying Dynamical Systems for Forecasting and Control -- 27. A Practical Guide to Applying Echo State Networks -- 28. Forecasting with Recurrent Neural Networks: 12 Tricks -- 29. Solving Partially Observable Reinforcement Learning Problems with Recurrent Neural Networks -- 30. 10 Steps and Some Tricks to Set up Neural Reinforcement Controllers. |
| Record Nr. | UNISA-996466274203316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Neural Networks: Tricks of the Trade [[electronic resource] /] / edited by Genevieve B. Orr, Klaus-Robert Müller
| Neural Networks: Tricks of the Trade [[electronic resource] /] / edited by Genevieve B. Orr, Klaus-Robert Müller |
| Edizione | [1st ed. 1998.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 1998 |
| Descrizione fisica | 1 online resource (VIII, 432 p.) |
| Disciplina | 006.3/2 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computers
Artificial intelligence Microprocessors Pattern recognition Computational complexity Computation by Abstract Devices Artificial Intelligence Processor Architectures Pattern Recognition Complexity |
| ISBN | 3-540-49430-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Speeding Learning -- Efficient BackProp -- Regularization Techniques to Improve Generalization -- Early Stopping - But When? -- A Simple Trick for Estimating the Weight Decay Parameter -- Controlling the hyperparameter search in MacKay’s Bayesian neural network framework -- Adaptive Regularization in Neural Network Modeling -- Large Ensemble Averaging -- Improving Network Models and Algorithmic Tricks -- Square Unit Augmented Radially Extended Multilayer Perceptrons -- A Dozen Tricks with Multitask Learning -- Solving the Ill-Conditioning in Neural Network Learning -- Centering Neural Network Gradient Factors -- Avoiding roundoff error in backpropagating derivatives -- Representing and Incorporating Prior Knowledge in Neural Network Training -- Transformation Invariance in Pattern Recognition — Tangent Distance and Tangent Propagation -- Combining Neural Networks and Context-Driven Search for Online, Printed Handwriting Recognition in the Newton -- Neural Network Classification and Prior Class Probabilities -- Applying Divide and Conquer to Large Scale Pattern Recognition Tasks -- Tricks for Time Series -- Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions -- How to Train Neural Networks. |
| Record Nr. | UNISA-996465914003316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 1998 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Neural Networks: Tricks of the Trade / / edited by Genevieve B. Orr, Klaus-Robert Müller
| Neural Networks: Tricks of the Trade / / edited by Genevieve B. Orr, Klaus-Robert Müller |
| Edizione | [1st ed. 1998.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 1998 |
| Descrizione fisica | 1 online resource (VIII, 432 p.) |
| Disciplina | 006.3/2 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computers
Artificial intelligence Microprocessors Pattern recognition Computational complexity Computation by Abstract Devices Artificial Intelligence Processor Architectures Pattern Recognition Complexity |
| ISBN | 3-540-49430-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Speeding Learning -- Efficient BackProp -- Regularization Techniques to Improve Generalization -- Early Stopping - But When? -- A Simple Trick for Estimating the Weight Decay Parameter -- Controlling the hyperparameter search in MacKay’s Bayesian neural network framework -- Adaptive Regularization in Neural Network Modeling -- Large Ensemble Averaging -- Improving Network Models and Algorithmic Tricks -- Square Unit Augmented Radially Extended Multilayer Perceptrons -- A Dozen Tricks with Multitask Learning -- Solving the Ill-Conditioning in Neural Network Learning -- Centering Neural Network Gradient Factors -- Avoiding roundoff error in backpropagating derivatives -- Representing and Incorporating Prior Knowledge in Neural Network Training -- Transformation Invariance in Pattern Recognition — Tangent Distance and Tangent Propagation -- Combining Neural Networks and Context-Driven Search for Online, Printed Handwriting Recognition in the Newton -- Neural Network Classification and Prior Class Probabilities -- Applying Divide and Conquer to Large Scale Pattern Recognition Tasks -- Tricks for Time Series -- Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions -- How to Train Neural Networks. |
| Record Nr. | UNINA-9910143467403321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 1998 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Pattern Recognition [[electronic resource] ] : 28th DAGM Symposium, Berlin, Germany, September 12-14, 2006, Proceedings / / edited by Katrin Franke, Klaus-Robert Müller, Bertram Nickolay, Ralf Schäfer
| Pattern Recognition [[electronic resource] ] : 28th DAGM Symposium, Berlin, Germany, September 12-14, 2006, Proceedings / / edited by Katrin Franke, Klaus-Robert Müller, Bertram Nickolay, Ralf Schäfer |
| Edizione | [1st ed. 2006.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 |
| Descrizione fisica | 1 online resource (XX, 784 p.) |
| Disciplina | 006.4/2 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Pattern recognition
Optical data processing Artificial intelligence Computer graphics Algorithms Pattern Recognition Image Processing and Computer Vision Artificial Intelligence Computer Graphics Algorithm Analysis and Problem Complexity |
| ISBN | 3-540-44414-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Image Filtering, Restoration and Segmentation -- Shape Analysis and Representation -- Recognition, Categorization and Detection -- Computer Vision and Image Retrieval -- Machine Learning and Statistical Data Analysis -- Biomedical Data Analysis -- Motion Analysis and Tracking -- Pose Recognition -- Stereo and Structure from Motion -- Multi-view Image and Geometric Processing -- 3D View Registration and Surface Modelling. |
| Record Nr. | UNISA-996466006403316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
