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[null,"1.","Record Nr.",null,"UNINA9910807841703321"]
[null,null,"Autore",null,"Austen Jane <1775-1817, >"]
[null,null,"Titolo",null,"Emma / / Jane Austen"]
[null,null,"Pubbl/distr/stampa",null,"New York, New York : , : Open Road Media Integrated Media, , 2014"]
[{"$nulls":4},"©1815"]
[null,null,"ISBN",null,"1-4804-7700-1"]
[null,null,"Descrizione fisica",null,"1 online resource (822 p.)"]
[null,null,"Disciplina",null,"813.6"]
[null,null,"Soggetti",null,"Young women"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,"Nota di contenuto",null,"Cover; Title Page; Contents; VOLUME I; CHAPTER I; CHAPTER II; CHAPTER III; CHAPTER IV; CHAPTER V; CHAPTER VI; CHAPTER VII; CHAPTER VIII; CHAPTER IX; CHAPTER X; CHAPTER XI; CHAPTER XII; CHAPTER XIII; CHAPTER XIV; CHAPTER XV; CHAPTER XVI; CHAPTER XVII; CHAPTER XVIII; VOLUME II; CHAPTER I; CHAPTER II; CHAPTER III; CHAPTER IV; CHAPTER V; CHAPTER VI; CHAPTER VII; CHAPTER VIII; CHAPTER IX; CHAPTER X; CHAPTER XI; CHAPTER XII; CHAPTER XIII; CHAPTER XIV; CHAPTER XV; CHAPTER XVI; CHAPTER XVII; CHAPTER XVIII; VOLUME III; CHAPTER I; CHAPTER II; CHAPTER III; CHAPTER IV; CHAPTER V; CHAPTER VI; CHAPTER VII"]
[{"$nulls":4},"CHAPTER VIIICHAPTER IX; CHAPTER X; CHAPTER XI; CHAPTER XII; CHAPTER XIII; CHAPTER XIV; CHAPTER XV; CHAPTER XVI; CHAPTER XVII; CHAPTER XVIII; CHAPTER XIX; Copyright"]
[null,null,null,"Sommario/riassunto","The timeless romance starring one of Jane Austen's most unforgettable charactersEmma Woodhouse is a privileged young woman whose greatest pleasure in life lies in matchmaking for anyone but herself. Written, by Austen's own admission, as \"a heroine whom no one but myself will much like,\" Emma's charm and wit exist in constant tension with her capacity for selfishness and vanity. Despite her intelligence, Emma stumbles from one catastrophe to the next-from a misguided attempt at securing a husband for her friend Harriet Smith to her disastrous meddling in the affairs of new arrivals Frank Churc"]
[null,"2.","Record Nr.",null,"UNINA9910483998803321"]
[null,null,"Titolo",null,"Evolutionary Machine Learning Techniques : Algorithms and Applications / / edited by Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2020"]
[null,null,"ISBN",null,"981-329-990-8"]
[null,null,"Edizione",null,"[1st ed. 2020.]"]
[null,null,"Descrizione fisica",null,"1 online resource (287 pages)"]
[null,null,"Collana",null,"Algorithms for Intelligent Systems, , 2524-7573"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Neural networks (Computer science)"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Mathematical Models of Cognitive Processes and Neural Networks"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book "]
[{"$nulls":4},"also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields."]