Rasoul karimi (18 resultados)
- Tapa blanda
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado - Como Nuevo
EUR 27,13
Envío por EUR 2,30Se envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: As New. Unread book in perfect condition.
- Tapa blanda
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 28,13
Envío por EUR 2,30Se envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
- Tapa blanda
Librería: BargainBookStores, Grand Rapids, MI, Estados Unidos de AmericaBargainBookStores
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 30,92
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 5 disponibles
Paperback or Softback. Condición: New. Active Learning for Recommender Systems. Book.
- Tapa blanda
Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 26,19
Envío por EUR 13,80Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. In.
- Tapa blanda
Librería: Chiron Media, Wallingford, , Reino UnidoChiron Media
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 23,17
Envío por EUR 17,85Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Paperback. Condición: New.
- Tapa blanda
Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 26,11
Envío por EUR 17,28Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
- Tapa blanda
Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado - Como Nuevo
EUR 30,14
Envío por EUR 17,28Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: As New. Unread book in perfect condition.
- Tapa blanda
Librería: preigu, Osnabrück, Alemaniapreigu
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 25,20
Envío por EUR 70,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 5 disponibles
Taschenbuch. Condición: Neu. Active Learning for Recommender Systems | Rasoul Karimi | Taschenbuch | 152 S. | Englisch | 2014 | Cuvillier | EAN 9783954046928 | Verantwortliche Person für die EU: Cuvillier Verlag, Nonnenstieg 8, 37075 Göttingen, info[at]cuvillier[dot]de | Anbieter: preigu.
- Tapa blanda
Librería: Buchpark, Trebbin, , AlemaniaBuchpark
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado
EUR 22,76
Envío por EUR 105,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Condición: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Nowadays we are living in an era that is overloaded with information. Decision-making in this environment can sometimes become a nightmare. There are too many choices and we simply cannot explore them all. Therefore, it would be really help…ful to have a system to help us to find the right choice. Such systems, which learn user preferences and provide personalized recommendations to them are called Recommender Systems. Evidently, the performance of recommender systems depends on the amount of information that users provide regarding items, most often in the form of ratings. This problem is amplified for new users because they have not provided any rating, which impacts negatively on the quality of generated recommendations. This problem is called new user problem or cold-start problem. A simple and effective way to overcome this problem, is by posing queries to new users so that they express their preferences about selected items, e.g. by rating them. Nevertheless, the selection of items must take into consideration that users are not willing to answer a lot of such queries. To address this problem, active learning methods have been proposed to acquire the most informative ratings, i.e ratings from users that will help most in determining their interests. The aim of this thesis is to take inspiration from the literature of active learning for machine learning and develop new methods for the new user problem in recommender systems. In the recommender system context, new users play the role of the Oracle and provide labels (ratings) to the queries (items). In this approach, we will take into consideration that although there are no data for new users, but there is abundant data for existing users. Such additional data can help us to develop scalable and accurate active learning methods for the new user problem in recommender systems. The thesis consists of two parts. In the first part, to be consistent with the settings of active learning in machine learning and the related works on the new user problem in recommender system, it is assumed that the new user is always able to rate the queried items. Next, this constraint is relaxed and new users are allowed not to rate the items. Most of the developed active learning methods exploit the characteristics matrix factorization because nevertheless, recent research (especially as has been demonstrated during the Netflix challenge) indicates that matrix factorization is a superior prediction model for recommender systems compared to other approaches.
- Tapa blanda
Librería: Mispah books, Redhill, SURRE, Reino UnidoMispah books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 118,67
Envío por EUR 28,80Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
paperback. Condición: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Advanced Biosensor Modelling with DNA Application: Accurate Modeling of DNA Hybridization
Karimi Feizabadi, Hediyeh, Rahmani, Rasoul, Ahmadi, Mohammad
- Tapa blanda
Librería: Mispah books, Redhill, SURRE, Reino UnidoMispah books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 169,70
Envío por EUR 28,80Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
paperback. Condición: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
- Tapa blanda
- Impresión bajo demanda
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 30,51
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
- Tapa blanda
- Impresión bajo demanda
Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 27,96
Envío por EUR 4,79Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
- Tapa blanda
- Impresión bajo demanda
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , AlemaniaBuchWeltWeit Ludwig Meier e.K.
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 27,60
Envío por EUR 23,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Nowadays we are living in an era that is overloaded with information. Decision-making in this environment can sometimes become a nightmare. There are too many choices and we simply cannot explore them all. Therefore, it would be rea…lly helpful to have a system to help us to find the right choice. Such systems, which learn user preferences and provide personalized recommendations to them are called Recommender Systems.Evidently, the performance of recommender systems depends on the amount of information that users provide regarding items, most often in the form of ratings. This problem is amplified for new users because they have not provided any rating, which impacts negatively on the quality of generated recommendations. This problem is called new user problem or cold-start problem. A simple and effective way to overcome this problem, is by posing queries to new users so that they express their preferences about selected items, e.g. by rating them. Nevertheless, the selection of items must take into consideration that users are not willing to answer a lot of such queries. To address this problem, active learning methods have been proposed to acquire the most informative ratings, i.e ratings from users that will help most in determining their interests.The aim of this thesis is to take inspiration from the literature of active learning for machine learning and develop new methods for the new user problem in recommender systems. In the recommender system context, new users play the role of the Oracle and provide labels (ratings) to the queries (items). In this approach, we will take into consideration that although there are no data for new users, but there is abundant data for existing users. Such additional data can help us to develop scalable and accurate active learning methods for the new user problem in recommender systems.The thesis consists of two parts. In the first part, to be consistent with the settings of active learning in machine learning and the related works on the new user problem in recommender system, it is assumed that the new user is always able to rate the queried items. Next, this constraint is relaxed and new users are allowed not to rate the items.Most of the developed active learning methods exploit the characteristics matrix factorization because nevertheless, recent research (especially as has been demonstrated during the Netflix challenge) indicates that matrix factorization is a superior prediction model for recommender systems compared to other approaches. 152 pp. Englisch.
- Tapa blanda
- Impresión bajo demanda
Librería: moluna, Greven, , Alemaniamoluna
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 25,09
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Über den AutorrnrnRasoul Karimi was born in 1980 in Tehran. He studied computer engineering and got his master degree in 2005 from the University of Tehran. He started his PhD in 2009 in Information System and Mac…hine Learning Lab (ISMLL), .
- Tapa blanda
- Impresión bajo demanda
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 27,60
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Nowadays we are living in an era that is overloaded with information. Decision-making in this environment can sometimes become a nightmare. There are too many choices and we simply cannot explore them all. Therefore, it would be really…helpful to have a system to help us to find the right choice. Such systems, which learn user preferences and provide personalized recommendations to them are called Recommender Systems.Evidently, the performance of recommender systems depends on the amount of information that users provide regarding items, most often in the form of ratings. This problem is amplified for new users because they have not provided any rating, which impacts negatively on the quality of generated recommendations. This problem is called new user problem or cold-start problem. A simple and effective way to overcome this problem, is by posing queries to new users so that they express their preferences about selected items, e.g. by rating them. Nevertheless, the selection of items must take into consideration that users are not willing to answer a lot of such queries. To address this problem, active learning methods have been proposed to acquire the most informative ratings, i.e ratings from users that will help most in determining their interests.The aim of this thesis is to take inspiration from the literature of active learning for machine learning and develop new methods for the new user problem in recommender systems. In the recommender system context, new users play the role of the Oracle and provide labels (ratings) to the queries (items). In this approach, we will take into consideration that although there are no data for new users, but there is abundant data for existing users. Such additional data can help us to develop scalable and accurate active learning methods for the new user problem in recommender systems.The thesis consists of two parts. In the first part, to be consistent with the settings of active learning in machine learning and the related works on the new user problem in recommender system, it is assumed that the new user is always able to rate the queried items. Next, this constraint is relaxed and new users are allowed not to rate the items.Most of the developed active learning methods exploit the characteristics matrix factorization because nevertheless, recent research (especially as has been demonstrated during the Netflix challenge) indicates that matrix factorization is a superior prediction model for recommender systems compared to other approaches.Cuvillier Verlag, Nonnenstieg 8, 37075 Göttingen 152 pp. Englisch.
- Tapa blanda
- Impresión bajo demanda
Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 29,54
Envío por EUR 61,04Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Nowadays we are living in an era that is overloaded with information. Decision-making in this environment can sometimes become a nightmare. There are too many choices and we simply cannot explore them all. Therefore, it would be really h…elpful to have a system to help us to find the right choice. Such systems, which learn user preferences and provide personalized recommendations to them are called Recommender Systems.Evidently, the performance of recommender systems depends on the amount of information that users provide regarding items, most often in the form of ratings. This problem is amplified for new users because they have not provided any rating, which impacts negatively on the quality of generated recommendations. This problem is called new user problem or cold-start problem. A simple and effective way to overcome this problem, is by posing queries to new users so that they express their preferences about selected items, e.g. by rating them. Nevertheless, the selection of items must take into consideration that users are not willing to answer a lot of such queries. To address this problem, active learning methods have been proposed to acquire the most informative ratings, i.e ratings from users that will help most in determining their interests.The aim of this thesis is to take inspiration from the literature of active learning for machine learning and develop new methods for the new user problem in recommender systems. In the recommender system context, new users play the role of the Oracle and provide labels (ratings) to the queries (items). In this approach, we will take into consideration that although there are no data for new users, but there is abundant data for existing users. Such additional data can help us to develop scalable and accurate active learning methods for the new user problem in recommender systems.The thesis consists of two parts. In the first part, to be consistent with the settings of active learning in machine learning and the related works on the new user problem in recommender system, it is assumed that the new user is always able to rate the queried items. Next, this constraint is relaxed and new users are allowed not to rate the items.Most of the developed active learning methods exploit the characteristics matrix factorization because nevertheless, recent research (especially as has been demonstrated during the Netflix challenge) indicates that matrix factorization is a superior prediction model for recommender systems compared to other approaches.
Advanced Biosensor Modelling with DNA Application
Hediyeh Karimi Feizabadi|Rasoul Rahmani|Mohammad Taghi Ahmadi
- Tapa blanda
- Impresión bajo demanda
Librería: moluna, Greven, , Alemaniamoluna
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 45,45
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Karimi Feizabadi HediyehDr. Hediyeh Karimi received the B.Sc. degree in Electrical Engineering- Electronics from the IAU university, Iran, in 2008 and M.Sc. and Ph.D. degrees in Electrical Engineering f…rom school of Malaysia-Japan In.
