02342nlm0 22006371i 450 9900092561704033219783540850939000925617FED01000925617(Aleph)000925617FED0100092561720100926d2008----km-y0itay50------baengDEdrnn-008mamaaInformation Theoretic SecurityRisorsa elettronicaThird International Conference, ICITS 2008, Calgary, Canada, August 10-13, 2008. Proceedingsedited by David Hutchison, Takeo Kanade, Josef Kittler, Jon M. Kleinberg, Friedemann Mattern, John C. Mitchell, Moni Naor, Oscar Nierstrasz, C. Pandu Rangan, Bernhard Steffen, Madhu Sudan, Demetri Terzopoulos, Doug Tygar, Moshe Y. Vardi, Gerhard Weikum, Reihaneh Safavi-NainiBerlin ; HeidelbergSpringer2008Lecture Notes in Computer Science0302-97435155Documento elettronicoTestoFormato html, pdfHutchison,DavidKanade,TakeoKittler,JosefKleinberg,Jon M.Mattern,FriedemannMitchell,John C.Naor,MoniNierstrasz,OscarPandu Rangan,C.Safavi-Naini,ReihanehSteffen,BernhardSudan,MadhuTerzopoulos,DemetriTygar,DougVardi,Moshe Y.Weikum,GerhardITUNINAREICATUNIMARCFull text per gli utenti Federico IIhttp://dx.doi.org/10.1007/978-3-540-85093-9EB990009256170403321Algorithm Analysis and Problem ComplexityComputer Communication NetworksComputer Communication NetworksComputer scienceComputer ScienceComputer softwareComputers and SocietyData EncryptionData encryption (Computer science)Data protectionInformation SystemsManagement of Computing and Information SystemsSystems and Data SecurityInformation Theoretic Security773803UNINA01096cam0 2200289 450 E60020005733220200720135117.020091202d1979 |||||ita|0103 baitaITDimostrazioni e confutazionila logica della scoperta matematicaImre Lakatosa cura di John Worrall e Elie Zaharintroduzione all'edizione italiana di Giulio GiorelloMilanoFeltrinelli1979230 p.ill.23 cmFilosofia della scienza19001LAEC000215082001 *Filosofia della scienza19Lakatos, ImreA60020004457707045135Worrall, JohnA600200058633070Zahar, ElieA600200058634070ITUNISOB20200720RICAUNISOBUNISOB10042408E600200057332M 102 Monografia moderna SBNM100003110Si42408AcquistocutoloUNISOBUNISOB20091202121045.020200720135105.0AlfanoDimostrazioni e confutazioni478410UNISOB03414nam 22007092 450 991045998670332120160211123446.01-107-21880-20-511-99432-X1-282-97834-997866129783400-511-97582-10-511-99209-20-511-99312-90-511-98930-X0-511-98752-80-511-99111-8(CKB)2670000000069769(EBL)647360(OCoLC)700706144(SSID)ssj0000473438(PQKBManifestationID)11331151(PQKBTitleCode)TC0000473438(PQKBWorkID)10437705(PQKB)10594099(UkCbUP)CR9780511975820(MiAaPQ)EBC647360(Au-PeEL)EBL647360(CaPaEBR)ebr10442834(CaONFJC)MIL297834(EXLCZ)99267000000006976920101011d2011|||| uy| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierStatistical learning for biomedical data /James D. Malley, Karen G. Malley, Sinisa Pajevic[electronic resource]Cambridge :Cambridge University Press,2011.1 online resource (xii, 285 pages) digital, PDF file(s)Practical guides to biostatistics and epidemiologyTitle from publisher's bibliographic system (viewed on 05 Oct 2015).0-521-69909-6 0-521-87580-3 Includes bibliographical references and index.pt. 1. Introduction -- pt. 2. A machine toolkit -- pt. 3. Analysis fundamentals -- pt. 4. Machine strategies.This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests™, neural nets, support vector machines, nearest neighbors and boosting.Practical guides to biostatistics and epidemiology.Medical statisticsData processingBiometryData processingMedical statisticsData processing.BiometryData processing.614.285Malley James D.442004Malley Karen G.Pajevic SinisaUkCbUPUkCbUPBOOK9910459986703321Statistical learning for biomedical data2451068UNINA