Autore	Brazdil Pavel
Edizione	[2nd ed.]
Pubbl/distr/stampa	Cham, : Springer Nature, 2022
Descrizione fisica	1 online resource (349 pages)
Disciplina	006.31
Altri autori (Persone)	van RijnJan N SoaresCarlos VanschorenJoaquin
Collana	Cognitive Technologies
Soggetto topico	Artificial intelligence Data mining Machine learning
Soggetto non controllato	Metalearning Automating Machine Learning (AutoML) Machine Learning Artificial Intelligence algorithm selection algorithm recommendation algorithm configuration hyperparameter optimization automating the workflow/pipeline design metalearning in ensemble construction metalearning in deep neural networks transfer learning algorithm recommendation for data streams automating data science Open Access
ISBN	3-030-67024-4
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Record Nr.	UNISA-996464544803316

Autore	Miao Yuxin
Pubbl/distr/stampa	Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
Descrizione fisica	1 electronic resource (602 p.)
Soggetto topico	Technology: general issues History of engineering & technology Environmental science, engineering & technology
Soggetto non controllato	UAS multiple sensors vegetation index leaf nitrogen accumulation plant nitrogen accumulation pasture quality airborne hyperspectral imaging random forest regression sun-induced chlorophyll fluorescence (SIF) SIF yield indices upward downward leaf nitrogen concentration (LNC) wheat (Triticum aestivum L.) laser-induced fluorescence leaf nitrogen concentration back-propagation neural network principal component analysis fluorescence characteristics canopy nitrogen density radiative transfer model hyperspectral winter wheat flooded rice pig slurry aerial remote sensing vegetation indices N recommendation approach Mediterranean conditions nitrogen vertical distribution plant geometry remote sensing maize UAV multispectral imagery LNC non-parametric regression red-edge NDRE dynamic change model sigmoid curve grain yield prediction leaf chlorophyll content red-edge reflectance spectral index precision N fertilization chlorophyll meter NDVI NNI canopy reflectance sensing N mineralization farmyard manures Triticum aestivum discrete wavelet transform partial least squares hyper-spectra rice nitrogen management reflectance index multiple variable linear regression Lasso model Multiplex®3 sensor nitrogen balance index nitrogen nutrition index nitrogen status diagnosis precision nitrogen management terrestrial laser scanning spectrometer plant height biomass nitrogen concentration precision agriculture unmanned aerial vehicle (UAV) digital camera leaf chlorophyll concentration portable chlorophyll meter crop PROSPECT-D sensitivity analysis UAV multispectral imagery spectral vegetation indices machine learning plant nutrition canopy spectrum non-destructive nitrogen status diagnosis drone multispectral camera SPAD smartphone photography fixed-wing UAV remote sensing random forest canopy reflectance crop N status Capsicum annuum proximal optical sensors Dualex sensor leaf position proximal sensing cross-validation feature selection hyperparameter tuning image processing image segmentation nitrogen fertilizer recommendation supervised regression RapidSCAN sensor nitrogen recommendation algorithm in-season nitrogen management nitrogen use efficiency yield potential yield responsiveness standard normal variate (SNV) continuous wavelet transform (CWT) wavelet features optimization competitive adaptive reweighted sampling (CARS) partial least square (PLS) grapevine hyperparameter optimization multispectral imaging precision viticulture RGB multispectral coverage adjusted spectral index vegetation coverage random frog algorithm active canopy sensing integrated sensing system discrete NIR spectral band data soil total nitrogen concentration moisture absorption correction index particle size correction index coupled elimination
ISBN	3-0365-5710-5
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Record Nr.	UNINA-9910637794503321

Autore	Paraforos Dimitrios
Pubbl/distr/stampa	MDPI - Multidisciplinary Digital Publishing Institute, 2022
Descrizione fisica	1 online resource (324 p.)
Soggetto topico	Geography Research & information: general
Soggetto non controllato	1D convolution neural networks 3D bale wrapping method 3D Convolutional Neural Network agricultural monitoring agriculture algorithms anomaly intrusion detection artificial neural network autonomous robots band selection Bidirectional long-short term memory Boufakrane River watershed canopy conductance CCA chlorophyll a chlorophyll-a concentration climate change clustering CNN coastal management Coatzacoalcos Comino computer vision convolutional neural networks Copernicus Sentinels crop classification crop fields crop mapping crop yield improvement crops crops diseases cubic SVM cucumber data analysis data augmentation data fusion deep learning deep transfer learning density estimation dissolved oxygen drone entropy environmental monitoring environmental protection equal bale dimensions evapotranspiration Explainable Artificial Intelligence Faster R-CNN features fusion food security forest roads geographic information system (GIS) GIS Google Earth Engine Gozo green ring green technologies guava disease histogram hyperparameter optimization hyperspectral hyperspectral imagery hyperspectral imaging hyperspectral remoting sensing IDS image classification internet of things Internet of Things internode-elongation Interreg invasive plants IoT IoT ecosystem irrigation requirements land use land use classification Land Use/Land Cover Landsat leaf disease LISS-III machine learning machine learning algorithm machine vision Malta mango leaf mathematical model metaheuristic minimal film consumption modeling modeling approach modified normalized difference water index (MNDWI) modular robot multi-temporal data multispectral natural resources NDVI nitrate nitrogen prediction normalized difference vegetation index (NDVI) object-based classification optimal bale dimensions ordinary kriging ordinary Kriging overfitting panicle initiation path planning penman-monteith equation pest control photogrammetry plant disease detection pocket beaches precision agriculture probe proximal sensing random forest rational sampling numbers remote sensing resource constraint RF rice farming rice plant round bales sampling SAR satellite image analysis selective spraying sensor Sentinel 1 and 2 Sentinel-1a Sentinel-2 Sicily simulation site-specific site-specific weed management smart agriculture soil attribute soil pH soil tillage spatial heterogeneity spatial variation sustainable agriculture sustainable environment sustainable land use SVM Synthetic Aperture Radar (SAR) systematic literature review temperature profile time series analysis transfer learning urban flood vein pattern virtual pests Vision Transformer vision-based crop and weed detection water extraction water quality water resources weed YOLOv5 Ziz basin
ISBN	3-0365-5338-X
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Altri titoli varianti	Sustainable Agriculture and Advances of Remote Sensing
Record Nr.	UNINA-9910619464703321

Autore	Brazdil Pavel
Edizione	[2nd ed.]
Pubbl/distr/stampa	Cham, : Springer Nature, 2022
Descrizione fisica	1 online resource (349 pages)
Disciplina	006.31
Altri autori (Persone)	van RijnJan N SoaresCarlos VanschorenJoaquin
Collana	Cognitive Technologies
Soggetto topico	Artificial intelligence Data mining Machine learning Aprenentatge automàtic Mineria de dades
Soggetto genere / forma	Llibres electrònics
Soggetto non controllato	Metalearning Automating Machine Learning (AutoML) Machine Learning Artificial Intelligence algorithm selection algorithm recommendation algorithm configuration hyperparameter optimization automating the workflow/pipeline design metalearning in ensemble construction metalearning in deep neural networks transfer learning algorithm recommendation for data streams automating data science Open Access
ISBN	3-030-67024-4
Classificazione	COM004000COM021030
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Intro -- Preface -- Contents -- Part I Basic Concepts and Architecture -- 1 Introduction -- 1.1 Organization of the Book -- 1.2 Basic Concepts and Architecture (Part I) -- 1.3 Advanced Techniques and Methods (Part II) -- 1.4 Repositories of Experimental Results (Part III) -- References -- 2 Metalearning Approaches for Algorithm Selection I (Exploiting Rankings) -- 2.1 Introduction -- 2.2 Different Forms of Recommendation -- 2.3 Ranking Models for Algorithm Selection -- 2.4 Using a Combined Measure of Accuracy and Runtime -- 2.5 Extensions and Other Approaches -- References -- 3 Evaluating Recommendations of Metalearning/AutoML Systems -- 3.1 Introduction -- 3.2 Methodology for Evaluating Base-Level Algorithms -- 3.3 Normalization of Performance for Base-Level Algorithms -- 3.4 Methodology for Evaluating Metalearning and AutoML Systems -- 3.5 Evaluating Recommendations by Correlation -- 3.6 Evaluating the Effects of Recommendations -- 3.7 Some Useful Measures -- References -- 4 Dataset Characteristics (Metafeatures) -- 4.1 Introduction -- 4.2 Data Characterization Used in Classification Tasks -- 4.3 Data Characterization Used in Regression Tasks -- 4.4 Data Characterization Used in Time Series Tasks -- 4.5 Data Characterization Used in Clustering Tasks -- 4.6 Deriving New Features from the Basic Set -- 4.7 Selection of Metafeatures -- 4.8 Algorithm-Specific Characterization and Representation Issues -- 4.9 Establishing Similarity Between Datasets -- References -- 5 Metalearning Approaches for Algorithm Selection II -- 5.1 Introduction -- 5.2 Using Regression Models in Metalearning Systems -- 5.3 Using Classification at Meta-level for the Prediction of Applicability -- 5.4 Methods Based on Pairwise Comparisons -- 5.5 Pairwise Approach for a Set of Algorithms -- 5.6 Iterative Approach of Conducting Pairwise Tests -- 5.7 Using ART Trees and Forests. 5.8 Active Testing -- 5.9 Non-propositional Approaches -- References -- 6 Metalearning for Hyperparameter Optimization -- 6.1 Introduction -- 6.2 Basic Hyperparameter Optimization Methods -- 6.3 Bayesian Optimization -- 6.4 Metalearning for Hyperparameter Optimization -- 6.5 Concluding Remarks -- References -- 7 Automating Workflow/Pipeline Design -- 7.1 Introduction -- 7.2 Constraining the Search in Automatic Workflow Design -- 7.3 Strategies Used in Workflow Design -- 7.4 Exploiting Rankings of Successful Plans (Workflows) -- References -- Part II Advanced Techniques and Methods -- 8 Setting Up Configuration Spaces and Experiments -- 8.1 Introduction -- 8.2 Types of Configuration Spaces -- 8.3 Adequacy of Configuration Spaces for Given Tasks -- 8.4 Hyperparameter Importance and Marginal Contribution -- 8.5 Reducing Configuration Spaces -- 8.6 Configuration Spaces in Symbolic Learning -- 8.7 Which Datasets Are Needed? -- 8.8 Complete versus Incomplete Metadata -- 8.9 Exploiting Strategies from Multi-armed Bandits to Schedule Experiments -- 8.10 Discussion -- References -- 9 Combining Base-Learners into Ensembles -- 9.1 Introduction -- 9.2 Bagging and Boosting -- 9.3 Stacking and Cascade Generalization -- 9.4 Cascading and Delegating -- 9.5 Arbitrating -- 9.6 Meta-decision Trees -- 9.7 Discussion -- References -- 10 Metalearning in Ensemble Methods -- 10.1 Introduction -- 10.2 Basic Characteristics of Ensemble Systems -- 10.3 Selection-Based Approaches for Ensemble Generation -- 10.4 Ensemble Learning (per Dataset) -- 10.5 Dynamic Selection of Models (per Instance) -- 10.6 Generation of Hierarchical Ensembles -- 10.7 Conclusions and Future Research -- References -- 11 Algorithm Recommendation for Data Streams -- 11.1 Introduction -- 11.2 Metafeature-Based Approaches -- 11.3 Data Stream Ensembles -- 11.4 Recurring Meta-level Models. 11.5 Challenges for Future Research -- References -- 12 Transfer of Knowledge Across Tasks -- 12.1 Introduction -- 12.2 Background, Terminology, and Notation -- 12.3 Learning Architectures in Transfer Learning -- 12.4 A Theoretical Framework -- References -- 13 Metalearning for Deep Neural Networks -- 13.1 Introduction -- 13.2 Background and Notation -- 13.3 Metric-Based Metalearning -- 13.4 Model-Based Metalearning -- 13.5 Optimization-Based Metalearning -- 13.6 Discussion and Outlook -- References -- 14 Automating Data Science -- 14.1 Introduction -- 14.2 Defining the Current Problem/Task -- 14.3 Identifying the Task Domain and Knowledge -- 14.4 Obtaining the Data -- 14.5 Automating Data Preprocessing and Transformation -- 14.6 Automating Model and Report Generation -- References -- 15 Automating the Design of Complex Systems -- 15.1 Introduction -- 15.2 Exploiting a Richer Set of Operators -- 15.3 Changing the Granularity by Introducing New Concepts -- 15.4 Reusing New Concepts in Further Learning -- 15.5 Iterative Learning -- 15.6 Learning to Solve Interdependent Tasks -- References -- Part III Organizing and Exploiting Metadata -- 16 Metadata Repositories -- 16.1 Introduction -- 16.2 Organizing the World Machine Learning Information -- 16.3 OpenML -- References -- 17 Learning from Metadata in Repositories -- 17.1 Introduction -- 17.2 Performance Analysis of Algorithms per Dataset -- 17.3 Performance Analysis of Algorithms across Datasets -- 17.4 Effect of Specific Data/Workflow Characteristics on Performance -- 17.5 Summary -- References -- 18 Concluding Remarks -- 18.1 Introduction -- 18.2 Form of Metaknowledge Used in Different Approaches -- 18.3 Future Challenges -- References -- Index.
Record Nr.	UNINA-9910548277503321