LEADER 01074nam0-22003731i-450- 001 990003748890403321 005 20040721142850.0 010 $a0-8032-7995-7 035 $a000374889 035 $aFED01000374889 035 $a(Aleph)000374889FED01 035 $a000374889 100 $a20030910d--------km-y0itay50------ba 101 0 $aeng 102 $aUS 105 $a--------001cy 200 1 $a<> Sokal hoax$ethe sham that shook the academy$fedited by the editors of Lingua Franca 210 $aLincoln$aLondon$cUniversity of Nebraska Press$dİ2000 215 $axii, 265 p.$d23 cm 300 $aContiene riferimenti bibl. 610 0 $aScienza$aFilosofia 610 0 $aScienza$aAspetti sociali 610 0 $aSokal Alan (fisico, 1955- )$aStudi 676 $a501$v21$zita 676 $a306.45$v21$zita 702 1$aSokal,$bAlan D. 712 $aLingua Franca 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003748890403321 952 $a501 SOK 2$b2392$fBFS 959 $aBFS 996 $aSokal hoax$9508358 997 $aUNINA LEADER 00907nam0-22002891i-450- 001 990002508100403321 005 20090204152548.0 035 $a000250810 035 $aFED01000250810 035 $a(Aleph)000250810FED01 035 $a000250810 100 $a20030910d1963----km-y0itay50------ba 101 0 $aita 200 1 $a<>programmazione della produzione$emetodi e procedimenti .... valersi dei mezzi meccanografici ed elettronici ai fini della produzione$fGiorgio Deangeli. 210 $aBologna$cZanichelli$d1963. 215 $aVIII, 225 p.$d24 cm 610 0 $aInformatica, Informatica di base 700 1$aDeangeli,$bGiorgio$0586 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990002508100403321 952 $aVII-A-14$b2169$fMAS 952 $aO 40$b687$fDINPA 959 $aMAS 959 $aDINPA 996 $aProgrammazione della Produzione$9434268 997 $aUNINA LEADER 05143nam 22006495 450 001 9910298411503321 005 20200630042427.0 010 $a3-319-96978-1 024 7 $a10.1007/978-3-319-96978-7 035 $a(CKB)4100000007110850 035 $a(MiAaPQ)EBC5592869 035 $a(DE-He213)978-3-319-96978-7 035 $a(PPN)23247365X 035 $a(EXLCZ)994100000007110850 100 $a20181105d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Ecology and Sustainable Natural Resource Management /$fedited by Grant Humphries, Dawn R. Magness, Falk Huettmann 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (441 pages) 311 $a3-319-96976-5 327 $a1: Introduction to Machine Learning A. Data-intensive science B. Data Issues and Availability -- 2: Data-mining in Ecological and Wildlife Research A. Multiple Methods in the Scientific Process B. Data-mining in Ecological and Wildlife Research C. Applications in Ecological Research a. Predicting Patterns in Space and Time b. Data Exploration and Hypothesis Generation c. Pattern Recognition for Sampling D. Bringing It All Together: Leveraging Multiple Methods to Increase Knowledge for Resource Management -- 3: Machine Learning and Resource Management A. Web-based Machine Learning Applications for Wildlife Management B. Linking Machine Learning in Management Applications C. Machine Learning and the Cloud for Natural Resource Applications D. The Global View: Hopes and Disappointments E. The Future of Machine Learning. 330 $aEcologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often ?messy? and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field. 606 $aEcology 606 $aBioinformatics 606 $aComputational biology 606 $aBiometry 606 $aData mining 606 $aPattern perception 606 $aEcology$3https://scigraph.springernature.com/ontologies/product-market-codes/L19007 606 $aComputer Appl. in Life Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/L17004 606 $aBiostatistics$3https://scigraph.springernature.com/ontologies/product-market-codes/L15020 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aEcology. 615 0$aBioinformatics. 615 0$aComputational biology. 615 0$aBiometry. 615 0$aData mining. 615 0$aPattern perception. 615 14$aEcology. 615 24$aComputer Appl. in Life Sciences. 615 24$aBiostatistics. 615 24$aData Mining and Knowledge Discovery. 615 24$aPattern Recognition. 676 $a577.0285 702 $aHumphries$b Grant$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMagness$b Dawn R$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHuettmann$b Falk$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910298411503321 996 $aMachine Learning for Ecology and Sustainable Natural Resource Management$92522660 997 $aUNINA