Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Machine learning in dentistry / / Ching-Chang Ko, Dinggang Shen, Li Wang, editors
| Machine learning in dentistry / / Ching-Chang Ko, Dinggang Shen, Li Wang, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (186 pages) |
| Disciplina | 006.31 |
| Collana | Management and industrial engineering |
| Soggetto topico |
Aprenentatge automàtic
Odontologia Machine learning |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-71881-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910495242003321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine learning in elite volleyball : integrating performance analysis, competition and training strategies / / Rabiu Muazu Musa [and five others]
| Machine learning in elite volleyball : integrating performance analysis, competition and training strategies / / Rabiu Muazu Musa [and five others] |
| Autore | Muazu Musa Rabiu |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (58 pages) |
| Disciplina | 006.31 |
| Collana | SpringerBriefs in Applied Sciences and Technology |
| Soggetto topico |
Volleyball - Data processing
Machine learning Voleibol Processament de dades Aprenentatge automàtic |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-16-3192-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466395503316 |
Muazu Musa Rabiu
|
||
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine learning in elite volleyball : integrating performance analysis, competition and training strategies / / Rabiu Muazu Musa [and five others]
| Machine learning in elite volleyball : integrating performance analysis, competition and training strategies / / Rabiu Muazu Musa [and five others] |
| Autore | Muazu Musa Rabiu |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (58 pages) |
| Disciplina | 006.31 |
| Collana | SpringerBriefs in Applied Sciences and Technology |
| Soggetto topico |
Volleyball - Data processing
Machine learning Voleibol Processament de dades Aprenentatge automàtic |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-16-3192-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910485595403321 |
|
Muazu Musa Rabiu
|
||
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mathematical foundations for data analysis / / Jeff M. Phillips
| Mathematical foundations for data analysis / / Jeff M. Phillips |
| Autore | Phillips Jeff M. |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (299 pages) |
| Disciplina | 006.312 |
| Collana | Springer Series in the Data Sciences |
| Soggetto topico |
Data mining - Mathematics
Machine learning - Mathematics Mineria de dades Aprenentatge automàtic Matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-62341-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Contents -- 1 Probability Review -- 1.1 Sample Spaces -- 1.2 Conditional Probability and Independence -- 1.3 Density Functions -- 1.4 Expected Value -- 1.5 Variance -- 1.6 Joint, Marginal, and Conditional Distributions -- 1.7 Bayes' Rule -- 1.7.1 Model Given Data -- 1.8 Bayesian Inference -- Exercises -- 2 Convergence and Sampling -- 2.1 Sampling and Estimation -- 2.2 Probably Approximately Correct (PAC) -- 2.3 Concentration of Measure -- 2.3.1 Markov Inequality -- 2.3.2 Chebyshev Inequality -- 2.3.3 Chernoff-Hoeffding Inequality -- 2.3.4 Union Bound and Examples -- 2.4 Importance Sampling -- 2.4.1 Sampling Without Replacement with Priority Sampling -- Exercises -- 3 Linear Algebra Review -- 3.1 Vectors and Matrices -- 3.2 Addition and Multiplication -- 3.3 Norms -- 3.4 Linear Independence -- 3.5 Rank -- 3.6 Square Matrices and Properties -- 3.7 Orthogonality -- Exercises -- 4 Distances and Nearest Neighbors -- 4.1 Metrics -- 4.2 Lp Distances and their Relatives -- 4.2.1 Lp Distances -- 4.2.2 Mahalanobis Distance -- 4.2.3 Cosine and Angular Distance -- 4.2.4 KL Divergence -- 4.3 Distances for Sets and Strings -- 4.3.1 Jaccard Distance -- 4.3.2 Edit Distance -- 4.4 Modeling Text with Distances -- 4.4.1 Bag-of-Words Vectors -- 4.4.2 k-Grams -- 4.5 Similarities -- 4.5.1 Set Similarities -- 4.5.2 Normed Similarities -- 4.5.3 Normed Similarities between Sets -- 4.6 Locality Sensitive Hashing -- 4.6.1 Properties of Locality Sensitive Hashing -- 4.6.2 Prototypical Tasks for LSH -- 4.6.3 Banding to Amplify LSH -- 4.6.4 LSH for Angular Distance -- 4.6.5 LSH for Euclidean Distance -- 4.6.6 Min Hashing as LSH for Jaccard Distance -- Exercises -- 5 Linear Regression -- 5.1 Simple Linear Regression -- 5.2 Linear Regression with Multiple Explanatory Variables -- 5.3 Polynomial Regression -- 5.4 Cross-Validation.
5.4.1 Other ways to Evaluate Linear Regression Models -- 5.5 Regularized Regression -- 5.5.1 Tikhonov Regularization for Ridge Regression -- 5.5.2 Lasso -- 5.5.3 Dual Constrained Formulation -- 5.5.4 Matching Pursuit -- Exercises -- 6 Gradient Descent -- 6.1 Functions -- 6.2 Gradients -- 6.3 Gradient Descent -- 6.3.1 Learning Rate -- 6.4 Fitting a Model to Data -- 6.4.1 Least Mean Squares Updates for Regression -- 6.4.2 Decomposable Functions -- Exercises -- 7 Dimensionality Reduction -- 7.1 Data Matrices -- 7.1.1 Projections -- 7.1.2 Sum of Squared Errors Goal -- 7.2 Singular Value Decomposition -- 7.2.1 Best Rank-k Approximation of a Matrix -- 7.3 Eigenvalues and Eigenvectors -- 7.4 The Power Method -- 7.5 Principal Component Analysis -- 7.6 Multidimensional Scaling -- 7.6.1 Why does Classical MDS work? -- 7.7 Linear Discriminant Analysis -- 7.8 Distance Metric Learning -- 7.9 Matrix Completion -- 7.10 Random Projections -- Exercises -- 8 Clustering -- 8.1 Voronoi Diagrams -- 8.1.1 Delaunay Triangulation -- 8.1.2 Connection to Assignment-Based Clustering -- 8.2 Gonzalez's Algorithm for k-Center Clustering -- 8.3 Lloyd's Algorithm for k-Means Clustering -- 8.3.1 Lloyd's Algorithm -- 8.3.2 k-Means++ -- 8.3.3 k-Mediod Clustering -- 8.3.4 Soft Clustering -- 8.4 Mixture of Gaussians -- 8.4.1 Expectation-Maximization -- 8.5 Hierarchical Clustering -- 8.6 Density-Based Clustering and Outliers -- 8.6.1 Outliers -- 8.7 Mean Shift Clustering -- Exercises -- 9 Classification -- 9.1 Linear Classifiers -- 9.1.1 Loss Functions -- 9.1.2 Cross-Validation and Regularization -- 9.2 Perceptron Algorithm -- 9.3 Support Vector Machines and Kernels -- 9.3.1 The Dual: Mistake Counter -- 9.3.2 Feature Expansion -- 9.3.3 Support Vector Machines -- 9.4 Learnability and VC dimension -- 9.5 kNN Classifiers -- 9.6 Decision Trees -- 9.7 Neural Networks. 9.7.1 Training with Back-propagation -- 10 Graph Structured Data -- 10.1 Markov Chains -- 10.1.1 Ergodic Markov Chains -- 10.1.2 Metropolis Algorithm -- 10.2 PageRank -- 10.3 Spectral Clustering on Graphs -- 10.3.1 Laplacians and their EigenStructures -- 10.4 Communities in Graphs -- 10.4.1 Preferential Attachment -- 10.4.2 Betweenness -- 10.4.3 Modularity -- Exercises -- 11 Big Data and Sketching -- 11.1 The Streaming Model -- 11.1.1 Mean and Variance -- 11.1.2 Reservoir Sampling -- 11.2 Frequent Items -- 11.2.1 Warm-Up: Majority -- 11.2.2 Misra-Gries Algorithm -- 11.2.3 Count-Min Sketch -- 11.2.4 Count Sketch -- 11.3 Matrix Sketching -- 11.3.1 Covariance Matrix Summation -- 11.3.2 Frequent Directions -- 11.3.3 Row Sampling -- 11.3.4 Random Projections and Count Sketch Hashing -- Exercises -- Index. |
| Record Nr. | UNINA-9910483358803321 |
|
Phillips Jeff M.
|
||
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mathematical foundations for data analysis / / Jeff M. Phillips
| Mathematical foundations for data analysis / / Jeff M. Phillips |
| Autore | Phillips Jeff M. |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (299 pages) |
| Disciplina | 006.312 |
| Collana | Springer Series in the Data Sciences |
| Soggetto topico |
Data mining - Mathematics
Machine learning - Mathematics Mineria de dades Aprenentatge automàtic Matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-62341-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Contents -- 1 Probability Review -- 1.1 Sample Spaces -- 1.2 Conditional Probability and Independence -- 1.3 Density Functions -- 1.4 Expected Value -- 1.5 Variance -- 1.6 Joint, Marginal, and Conditional Distributions -- 1.7 Bayes' Rule -- 1.7.1 Model Given Data -- 1.8 Bayesian Inference -- Exercises -- 2 Convergence and Sampling -- 2.1 Sampling and Estimation -- 2.2 Probably Approximately Correct (PAC) -- 2.3 Concentration of Measure -- 2.3.1 Markov Inequality -- 2.3.2 Chebyshev Inequality -- 2.3.3 Chernoff-Hoeffding Inequality -- 2.3.4 Union Bound and Examples -- 2.4 Importance Sampling -- 2.4.1 Sampling Without Replacement with Priority Sampling -- Exercises -- 3 Linear Algebra Review -- 3.1 Vectors and Matrices -- 3.2 Addition and Multiplication -- 3.3 Norms -- 3.4 Linear Independence -- 3.5 Rank -- 3.6 Square Matrices and Properties -- 3.7 Orthogonality -- Exercises -- 4 Distances and Nearest Neighbors -- 4.1 Metrics -- 4.2 Lp Distances and their Relatives -- 4.2.1 Lp Distances -- 4.2.2 Mahalanobis Distance -- 4.2.3 Cosine and Angular Distance -- 4.2.4 KL Divergence -- 4.3 Distances for Sets and Strings -- 4.3.1 Jaccard Distance -- 4.3.2 Edit Distance -- 4.4 Modeling Text with Distances -- 4.4.1 Bag-of-Words Vectors -- 4.4.2 k-Grams -- 4.5 Similarities -- 4.5.1 Set Similarities -- 4.5.2 Normed Similarities -- 4.5.3 Normed Similarities between Sets -- 4.6 Locality Sensitive Hashing -- 4.6.1 Properties of Locality Sensitive Hashing -- 4.6.2 Prototypical Tasks for LSH -- 4.6.3 Banding to Amplify LSH -- 4.6.4 LSH for Angular Distance -- 4.6.5 LSH for Euclidean Distance -- 4.6.6 Min Hashing as LSH for Jaccard Distance -- Exercises -- 5 Linear Regression -- 5.1 Simple Linear Regression -- 5.2 Linear Regression with Multiple Explanatory Variables -- 5.3 Polynomial Regression -- 5.4 Cross-Validation.
5.4.1 Other ways to Evaluate Linear Regression Models -- 5.5 Regularized Regression -- 5.5.1 Tikhonov Regularization for Ridge Regression -- 5.5.2 Lasso -- 5.5.3 Dual Constrained Formulation -- 5.5.4 Matching Pursuit -- Exercises -- 6 Gradient Descent -- 6.1 Functions -- 6.2 Gradients -- 6.3 Gradient Descent -- 6.3.1 Learning Rate -- 6.4 Fitting a Model to Data -- 6.4.1 Least Mean Squares Updates for Regression -- 6.4.2 Decomposable Functions -- Exercises -- 7 Dimensionality Reduction -- 7.1 Data Matrices -- 7.1.1 Projections -- 7.1.2 Sum of Squared Errors Goal -- 7.2 Singular Value Decomposition -- 7.2.1 Best Rank-k Approximation of a Matrix -- 7.3 Eigenvalues and Eigenvectors -- 7.4 The Power Method -- 7.5 Principal Component Analysis -- 7.6 Multidimensional Scaling -- 7.6.1 Why does Classical MDS work? -- 7.7 Linear Discriminant Analysis -- 7.8 Distance Metric Learning -- 7.9 Matrix Completion -- 7.10 Random Projections -- Exercises -- 8 Clustering -- 8.1 Voronoi Diagrams -- 8.1.1 Delaunay Triangulation -- 8.1.2 Connection to Assignment-Based Clustering -- 8.2 Gonzalez's Algorithm for k-Center Clustering -- 8.3 Lloyd's Algorithm for k-Means Clustering -- 8.3.1 Lloyd's Algorithm -- 8.3.2 k-Means++ -- 8.3.3 k-Mediod Clustering -- 8.3.4 Soft Clustering -- 8.4 Mixture of Gaussians -- 8.4.1 Expectation-Maximization -- 8.5 Hierarchical Clustering -- 8.6 Density-Based Clustering and Outliers -- 8.6.1 Outliers -- 8.7 Mean Shift Clustering -- Exercises -- 9 Classification -- 9.1 Linear Classifiers -- 9.1.1 Loss Functions -- 9.1.2 Cross-Validation and Regularization -- 9.2 Perceptron Algorithm -- 9.3 Support Vector Machines and Kernels -- 9.3.1 The Dual: Mistake Counter -- 9.3.2 Feature Expansion -- 9.3.3 Support Vector Machines -- 9.4 Learnability and VC dimension -- 9.5 kNN Classifiers -- 9.6 Decision Trees -- 9.7 Neural Networks. 9.7.1 Training with Back-propagation -- 10 Graph Structured Data -- 10.1 Markov Chains -- 10.1.1 Ergodic Markov Chains -- 10.1.2 Metropolis Algorithm -- 10.2 PageRank -- 10.3 Spectral Clustering on Graphs -- 10.3.1 Laplacians and their EigenStructures -- 10.4 Communities in Graphs -- 10.4.1 Preferential Attachment -- 10.4.2 Betweenness -- 10.4.3 Modularity -- Exercises -- 11 Big Data and Sketching -- 11.1 The Streaming Model -- 11.1.1 Mean and Variance -- 11.1.2 Reservoir Sampling -- 11.2 Frequent Items -- 11.2.1 Warm-Up: Majority -- 11.2.2 Misra-Gries Algorithm -- 11.2.3 Count-Min Sketch -- 11.2.4 Count Sketch -- 11.3 Matrix Sketching -- 11.3.1 Covariance Matrix Summation -- 11.3.2 Frequent Directions -- 11.3.3 Row Sampling -- 11.3.4 Random Projections and Count Sketch Hashing -- Exercises -- Index. |
| Record Nr. | UNISA-996466554403316 |
|
Phillips Jeff M.
|
||
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Minimum divergence methods in statistical machine learning : from an information geometric viewpoint / / Shinto Eguchi and Osamu Komori
| Minimum divergence methods in statistical machine learning : from an information geometric viewpoint / / Shinto Eguchi and Osamu Komori |
| Autore | Eguchi Shinto |
| Pubbl/distr/stampa | Tokyo, Japan : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (224 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Pattern recognition systems
Mathematics Aprenentatge automàtic Estadística matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 4-431-56922-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910552748103321 |
|
Eguchi Shinto
|
||
| Tokyo, Japan : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Minimum divergence methods in statistical machine learning : from an information geometric viewpoint / / Shinto Eguchi and Osamu Komori
| Minimum divergence methods in statistical machine learning : from an information geometric viewpoint / / Shinto Eguchi and Osamu Komori |
| Autore | Eguchi Shinto |
| Pubbl/distr/stampa | Tokyo, Japan : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (224 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Pattern recognition systems
Mathematics Aprenentatge automàtic Estadística matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 4-431-56922-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466419103316 |
|
Eguchi Shinto
|
||
| Tokyo, Japan : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Multimodal Affective Computing : Technologies and Applications in Learning Environments / / by Ramón Zatarain Cabada, Héctor Manuel Cárdenas López, Hugo Jair Escalante
| Multimodal Affective Computing : Technologies and Applications in Learning Environments / / by Ramón Zatarain Cabada, Héctor Manuel Cárdenas López, Hugo Jair Escalante |
| Autore | Cabada Ramón Zatarain |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (211 pages) |
| Disciplina | 371.334 |
| Altri autori (Persone) |
LópezHéctor Manuel Cárdenas
EscalanteHugo Jair |
| Soggetto topico |
Machine learning
Learning, Psychology of Pattern recognition systems User interfaces (Computer systems) Human-computer interaction Machine Learning Learning Theory Automated Pattern Recognition User Interfaces and Human Computer Interaction Aprenentatge automàtic Interacció persona-ordinador Interfícies d'usuari (Sistemes d'ordinadors) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-32542-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I: Fundamentals -- Chapter 1. Affective Computing -- Chapter 2. Machine learning and pattern recognition in affective computing -- Chapter 3. Affective Learning Environments -- Part II: Sentiment Analysis for Learning Environments -- Chapter 4. Building resources for sentiment detection -- Chapter 5. Methods for data representation -- Chapter 6. Designing and testing the classification models -- Chapter 7. Model integration to a learning system -- Part III: Multimodal Recognition of Learning-Oriented Emotions -- Chapter 8. Building Resources for Emotion Detection -- Chapter 9. Methods for Data Representation -- Chapter 10. Multimodal recognition systems -- Chapter 11. Multimodal emotion recognition in learning environments -- Part IV: Automatic Personality Recognition -- Chapter 12. Building resources for personality recognition -- Chapter 13. Methods for data representation -- Chapter 14. Personality recognition models -- Chapter 15. Multimodal personality recognition for affective computing. |
| Record Nr. | UNINA-9910734829703321 |
|
Cabada Ramón Zatarain
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Neural Networks and Deep Learning : A Textbook / / by Charu C. Aggarwal
| Neural Networks and Deep Learning : A Textbook / / by Charu C. Aggarwal |
| Autore | Aggarwal Charu C. |
| Edizione | [2nd ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (xxiv, 529 pages) : illustrations |
| Disciplina | 006.32 |
| Soggetto topico |
Machine learning
Data mining Artificial intelligence Expert systems (Computer science) Natural language processing (Computer science) Machine Learning Data Mining and Knowledge Discovery Artificial Intelligence Knowledge Based Systems Natural Language Processing (NLP) Xarxes neuronals (Informàtica) Aprenentatge automàtic |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-29642-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | An Introduction to Neural Networks -- The Backpropagation Algorithm -- Machine Learning with Shallow Neural Networks -- Deep Learning: Principles and Training Algorithms -- Teaching a Deep Neural Network to Generalize -- Radial Basis Function Networks -- Restricted Boltzmann Machines -- Recurrent Neural Networks -- Convolutional Neural Networks -- Graph Neural Networks -- Deep Reinforcement Learning -- Advanced Topics in Deep Learning. |
| Record Nr. | UNINA-9910734836503321 |
|
Aggarwal Charu C.
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Optimization and Data Science: Trends and Applications [[electronic resource] ] : 5th AIROYoung Workshop and AIRO PhD School 2021 Joint Event / / edited by Adriano Masone, Veronica Dal Sasso, Valentina Morandi
| Optimization and Data Science: Trends and Applications [[electronic resource] ] : 5th AIROYoung Workshop and AIRO PhD School 2021 Joint Event / / edited by Adriano Masone, Veronica Dal Sasso, Valentina Morandi |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (189 pages) |
| Disciplina | 519.6 |
| Collana | AIRO Springer Series |
| Soggetto topico |
Operations research
Management science Computer science—Mathematics Discrete mathematics Operations Research, Management Science Discrete Mathematics in Computer Science Optimització matemàtica Aprenentatge automàtic |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-86286-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910767533403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Soggetto topico
-
Aprenentatge automàtic
(98)
- Machine learning (62)
- Processament de dades (16)
- Intel·ligència artificial (15)
- Machine Learning (14)