LEADER 01808nam 2200421Ia 450 001 996385049903316 005 20200824132853.0 035 $a(CKB)4940000000074514 035 $a(EEBO)2248516633 035 $a(OCoLC)ocm12001026e 035 $a(OCoLC)12001026 035 $a(EXLCZ)994940000000074514 100 $a19850506d1652 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 12$aA congregational church is a catholike visible church, or, An examination of M. Hudson his vindication concerning the integrality of the catholike visible church$b[electronic resource] $ewherein also satisfaction is given to what M. Cawdrey writes touching that subject, in his review of M. Hooker's Survey of church discipline /$fby Samuel Stone .. 210 $aLondon $cPrinted by Peter Cole ...$d1652 215 $a[52] p 300 $aCawdrey's remarks on Mr. Hooker's Survey of church discipline occur in his Inconsistencies of the independent way. 300 $a"To the reader" signed: Samuel Mather. 300 $a"To my Reverend dear brother, M. Samuel Stone" by John Cotton (p. [8]) in verse. 300 $aErrata: p. [52]. 300 $aReproduction of original in Harvard University Libraries. 330 $aeebo-0062 606 $aChurch polity 615 0$aChurch polity. 700 $aStone$b Samuel$f1602-1663.$01006590 701 $aMather$b Samuel$f1626-1671.$01002681 701 $aCotton$b John$f1584-1652.$0793681 801 0$bEAA 801 1$bEAA 801 2$bm/c 801 2$bUMI 801 2$bWaOLN 906 $aBOOK 912 $a996385049903316 996 $aA congregational church is a catholike visible church, or, An examination of M. Hudson his vindication concerning the integrality of the catholike visible church$92352463 997 $aUNISA LEADER 04060nam 22006255 450 001 9910136001703321 005 20250505163232.0 010 $a3-319-47898-2 024 7 $a10.1007/978-3-319-47898-2 035 $a(CKB)3710000000909174 035 $a(DE-He213)978-3-319-47898-2 035 $a(MiAaPQ)EBC4721337 035 $a(PPN)196324491 035 $a(EXLCZ)993710000000909174 100 $a20161007d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Big Data $eProceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece /$fedited by Plamen Angelov, Yannis Manolopoulos, Lazaros Iliadis, Asim Roy, Marley Vellasco 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XVII, 348 p. 101 illus.) 225 1 $aAdvances in Intelligent Systems and Computing,$x2194-5365 ;$v529 311 08$a3-319-47897-4 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aPredicting human behavior based on web search activity: Greek referendum of 2015 -- Compact Video Description and Representation for Automated Summarization of Human Activities -- Attribute Learning for Network Intrusion Detection -- A Fast Deep Convolutional Neural Network for face detection in Big Visual Data -- Learning Symbols by Neural Network -- Designing HMMs models in the age of Big Data -- Extended Formulations for Online Action Selection on Big Action Sets -- Multi-Task Deep Neural Networks for Automated Extraction of Primary Site and Laterality Information from Cancer Pathology Reports -- An infrastructure and approach for infering knowledge over Big Data in the Vehicle Insurance Industry -- Unified Retrieval Model of Big Data -- Adaptive Elitist Differential Evolution Extreme Learning Machines on Big Data: Intelligent Recognition of Invasive Species. 330 $aThe book offers a timely snapshot of neural network technologies as a significant component of big data analytics platforms. It promotes new advances and research directions in efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms); implementations on different computing platforms (e.g. neuromorphic, graphics processing units (GPUs), clouds, clusters); and big data analytics applications to solve real-world problems (e.g. weather prediction, transportation, energy management). The book, which reports on the second edition of the INNS Conference on Big Data, held on October 23?25, 2016, in Thessaloniki, Greece, depicts an interesting collaborative adventure of neural networks with big data and other learning technologies. 410 0$aAdvances in Intelligent Systems and Computing,$x2194-5365 ;$v529 606 $aComputational intelligence 606 $aData mining 606 $aArtificial intelligence 606 $aComputational Intelligence 606 $aData Mining and Knowledge Discovery 606 $aArtificial Intelligence 615 0$aComputational intelligence. 615 0$aData mining. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aArtificial Intelligence. 676 $a005 702 $aAngelov$b Plamen$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aManolopoulos$b Yannis$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aIliadis$b Lazaros$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRoy$b Asim$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aVellasco$b Marley$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910136001703321 996 $aAdvances in Big Data$92123403 997 $aUNINA