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1. |
Record Nr. |
UNISA996223742403316 |
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Titolo |
Rivista di diritto processuale |
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Pubbl/distr/stampa |
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Descrizione fisica |
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Soggetti |
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Civil procedure - Italy |
Procedure (Law) |
Civil procedure |
Procesrecht |
Procediment civil |
Dret processal |
Procediment penal - Itàlia |
Dret processal - Itàlia |
Periodicals. |
Revistes electròniques |
Italy |
Itàlia |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Periodico |
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Note generali |
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2. |
Record Nr. |
UNINA9910921745903321 |
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Autore |
Ercole, Ludovico |
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Titolo |
Contro la "giustizia predittiva" : per una lettura conservativa del principio di certezza del diritto / Ludovico Ercole |
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Pubbl/distr/stampa |
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Torino, : Giappichelli, c2024 |
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ISBN |
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Descrizione fisica |
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Collana |
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Studi del Dipartimento di giurisprudenza Luiss Guido Carli. Sezione monografie ; 6 |
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Disciplina |
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Locazione |
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Collocazione |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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3. |
Record Nr. |
UNINA9910631083703321 |
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Autore |
Yuan Ye |
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Titolo |
Latent Factor Analysis for High-dimensional and Sparse Matrices : A particle swarm optimization-based approach / / by Ye Yuan, Xin Luo |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 |
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ISBN |
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Edizione |
[1st ed. 2022.] |
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Descrizione fisica |
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1 online resource (99 pages) |
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Collana |
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SpringerBriefs in Computer Science, , 2191-5776 |
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Disciplina |
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Soggetti |
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Artificial intelligence - Data processing |
Quantitative research |
Data mining |
Data Science |
Data Analysis and Big Data |
Data Mining and Knowledge Discovery |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Chapter 1. Introduction -- Chapter 2. Learning rate-free Latent Factor Analysis via PSO -- Chapter 3. Learning Rate and Regularization Coefficient-free Latent Factor Analysis via PSO -- Chapter 4. Regularization and Momentum Coefficient-free Non-negative Latent Factor Analysis via PSO -- Chapter 5. Advanced Learning rate-free Latent Factor Analysis via P2SO -- Chapter 6. Conclusion and Discussion. |
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Sommario/riassunto |
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Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question. This is the first book to focus on how particle swarm optimization can be |
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incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications. The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed. |
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