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Ajouter au panierPaperback. Etat : Very Good. Ex-library paperback in very nice condition with the usual markings and attachments.
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Ajouter au panierGebundene Ausgabe. Etat : Neu. Neu Neuware; teils original eingeschweisst; Rechnung mit MwSt.; new item, still sealed; -Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation). 288 pp. Englisch.
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Ajouter au panierGebundene Ausgabe. Etat : Neu. Neu Neuware; teils original eingeschweisst; Rechnung mit MwSt.; new item, still sealed; -Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation). 288 pp. Englisch.
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Ajouter au panierPaperback or Softback. Etat : New. Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics: 9th European Conference, EvoBIO 2011, Torino, Italy, April 27-29, 2011, Book.
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Ajouter au panierPaperback or Softback. Etat : New. Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics: 7th European Conference, Evobio 2009 T�bingen, Germany, April 15-17, 200. Book.
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Ajouter au panierEtat : New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-12 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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Langue: anglais
Edité par Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2010
ISBN 10 : 3642122108 ISBN 13 : 9783642122101
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Ajouter au panierPaperback. Etat : new. Paperback. The ?eld of bioinformatics has two main objectives: the creation and main- nance of biological databases, and the discovery of knowledge from life sciences datainordertounravelthemysteriesofbiologicalfunction,leadingtonewdrugs andtherapiesforhumandisease. Life sciencesdatacomeinthe formofbiological sequences, structures, pathways, or literature. One major aspect of discovering biological knowledge is to search, predict, or model speci?c information in a given dataset in order to generate new interesting knowledge. Computer science methods such as evolutionary computation, machine learning, and data mining all have a great deal to o?er the ?eld of bioinformatics. The goal of the 8th - ropean Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics (EvoBIO 2010) was to bring together experts in these ?elds in order to discuss new and novel methods for tackling complex biological problems. The 8th EvoBIO conference was held in Istanbul, Turkey during April 7-9, 2010attheIstanbulTechnicalUniversity.EvoBIO2010washeldjointly withthe 13th European Conference on Genetic Programming (EuroGP 2010), the 10th European Conference on Evolutionary Computation in Combinatorial Opti- sation (EvoCOP 2010), and the conference on the applications of evolutionary computation,EvoApplications. Collectively,the conferences areorganizedunder the name Evo* (www. evostar. org). EvoBIO, held annually as a workshop since 2003, became a conference in 2007 and it is now the premiere European event for those interested in the interface between evolutionary computation, machine learning, data mining, bioinformatics, and computational biology. Constitutes the refereed proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2010, held in Istanbul, Turkey, in April 2010 co-located with the Evo* 2010 events. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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