LEADER 01307nam 2200313Ia 450 001 996387090903316 005 20221108015608.0 035 $a(CKB)4940000000083493 035 $a(EEBO)2240862579 035 $a(OCoLC)19728867 035 $a(EXLCZ)994940000000083493 100 $a19890518d1644 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 12$aA true copie of Colonel Sr. Gamaliel Dudley's letter to His Highnesse Prince Rupert from Newark 4. March. 1644$b[electronic resource] $ebeing an exact relation of Sr. Marm. Langdale's march northward, as also the great victory obtained by him over Lord Fairfax neare Pontefract 1. Martii, 1644 210 $aOxford $cPrinted by Leonard Lichfield ...$d1644 215 $a[2], 6 p 300 $aReproduction of original in the Bodleian Library. 330 $aeebo-0014 607 $aGreat Britain$xHistory$yCivil War, 1642-1649 607 $aGreat Britain$xHistory, Military$y1603-1714 700 $aDudley$b Gamaliel$01019353 701 $aRupert$cPrince, Count Palatine,$f1619-1682.$01002175 801 1$bEAF 801 2$bWaOLN 906 $aBOOK 912 $a996387090903316 996 $aA true copie of Colonel Sr. Gamaliel Dudley's letter to His Highnesse Prince Rupert from Newark 4. March. 1644$92402544 997 $aUNISA LEADER 01440nam 2200373 450 001 9910688190303321 005 20230625092611.0 035 $a(CKB)5580000000514289 035 $a(NjHacI)995580000000514289 035 $a(EXLCZ)995580000000514289 100 $a20230625d2022 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aPrunus $eRecent Advances /$fedited by Ayzin B. Ku?den and Ali Ku?den 210 1$aLondon, United Kingdom :$cIntechOpen,$d2022. 215 $a1 online resource (212 pages) $cillustrations 311 $a1-83969-583-8 330 $aPrunus is one of the most important genera of fruit. It includes peaches, plums, cherries, apricots, and other stone fruits. This book discusses breeding, germplasm, fruit tree physiology, pruning, production, and nutritional studies of the Prunus species. It includes two sections on "Molecular and Breeding Studies and Germplasm Diversity in Prunus Species" and "Physiological and Nutritional Studies on Prunus Species." 606 $aFruit 606 $aGermplasm resources 615 0$aFruit. 615 0$aGermplasm resources. 676 $a641.34 702 $aKu?den$b Ayzin B. 702 $aKu?den$b Ali 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910688190303321 996 $aPrunus$92123488 997 $aUNINA LEADER 08506nam 22008055 450 001 9910484309003321 005 20251113183911.0 010 $a3-540-73871-1 024 7 $a10.1007/978-3-540-73871-8 035 $a(CKB)1000000000490209 035 $a(SSID)ssj0000315718 035 $a(PQKBManifestationID)11212740 035 $a(PQKBTitleCode)TC0000315718 035 $a(PQKBWorkID)10255226 035 $a(PQKB)10502823 035 $a(DE-He213)978-3-540-73871-8 035 $a(MiAaPQ)EBC3063397 035 $a(MiAaPQ)EBC6413195 035 $a(PPN)123164036 035 $a(EXLCZ)991000000000490209 100 $a20100301d2007 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAdvanced Data Mining and Applications $eThird International Conference, ADMA 2007, Harbin, China, August 6-8, 2007 Proceedings /$fedited by Reda Alhajj, Hong Gao, Xue Li, Jianzhong Li, Osmar R. Zaiane 205 $a1st ed. 2007. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2007. 215 $a1 online resource (XVI, 636 p. 201 illus.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v4632 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-540-73870-3 320 $aIncludes bibliographical references and index. 327 $aInvited Talk -- Mining Ambiguous Data with Multi-instance Multi-label Representation -- Regular Papers -- DELAY: A Lazy Approach for Mining Frequent Patterns over High Speed Data Streams -- Exploring Content and Linkage Structures for Searching Relevant Web Pages -- CLBCRA-Approach for Combination of Content-Based and Link-Based Ranking in Web Search -- Rough Sets in Hybrid Soft Computing Systems -- Discovering Novel Multistage Attack Strategies -- Privacy Preserving DBSCAN Algorithm for Clustering -- A New Multi-level Algorithm Based on Particle Swarm Optimization for Bisecting Graph -- A Supervised Subspace Learning Algorithm: Supervised Neighborhood Preserving Embedding -- A k-Anonymity Clustering Method for Effective Data Privacy Preservation -- LSSVM with Fuzzy Pre-processing Model Based Aero Engine Data Mining Technology -- A Coding Hierarchy Computing Based Clustering Algorithm -- Mining Both Positive and Negative Association Rules from Frequent and Infrequent Itemsets -- Survey of Improving Naive Bayes for Classification -- Privacy Preserving BIRCH Algorithm for Clustering over Arbitrarily Partitioned Databases -- Unsupervised Outlier Detection in Sensor Networks Using Aggregation Tree -- Separator: Sifting Hierarchical Heavy Hitters Accurately from Data Streams -- Spatial Fuzzy Clustering Using Varying Coefficients -- Collaborative Target Classification for Image Recognition in Wireless Sensor Networks -- Dimensionality Reduction for Mass Spectrometry Data -- The Study of Dynamic Aggregation of Relational Attributes on Relational Data Mining -- Learning Optimal Kernel from Distance Metric in Twin Kernel Embedding for Dimensionality Reduction and Visualization of Fingerprints -- Efficiently Monitoring Nearest Neighbors to a Moving Object -- A Novel Text Classification Approach Based onEnhanced Association Rule -- Applications of the Moving Average of n th -Order Difference Algorithm for Time Series Prediction -- Inference of Gene Regulatory Network by Bayesian Network Using Metropolis-Hastings Algorithm -- A Consensus Recommender for Web Users -- Constructing Classification Rules Based on SVR and Its Derivative Characteristics -- Hiding Sensitive Associative Classification Rule by Data Reduction -- AOG-ags Algorithms and Applications -- A Framework for Titled Document Categorization with Modified Multinomial Naivebayes Classifier -- Prediction of Protein Subcellular Locations by Combining K-Local Hyperplane Distance Nearest Neighbor -- A Similarity Retrieval Method in Brain Image Sequence Database -- A Criterion for Learning the Data-Dependent Kernel for Classification -- Topic Extraction with AGAPE -- Clustering Massive Text Data Streams by Semantic Smoothing Model -- GraSeq: A Novel Approximate Mining Approach of Sequential Patterns over Data Stream -- A Novel Greedy Bayesian Network Structure Learning Algorithm for Limited Data -- Optimum Neural Network Construction Via Linear Programming Minimum Sphere Set Covering -- How Investigative Data Mining Can Help Intelligence Agencies to Discover Dependence of Nodes in Terrorist Networks -- Prediction of Enzyme Class by Using Reactive Motifs Generated from Binding and Catalytic Sites -- Bayesian Network Structure Ensemble Learning -- Fusion of Palmprint and Iris for Personal Authentication -- Enhanced Graph Based Genealogical Record Linkage -- A Fuzzy Comprehensive Clustering Method -- Short Papers -- CACS: A Novel Classification Algorithm Based on Concept Similarity -- Data Mining in Tourism Demand Analysis: A Retrospective Analysis -- Chinese Patent Mining Based on Sememe Statistics and Key-Phrase Extraction -- Classification of Business Travelers Using SVMs Combined with Kernel Principal Component Analysis -- Research on the Traffic Matrix Based on Sampling Model -- A Causal Analysis for the Expenditure Data of Business Travelers -- A Visual and Interactive Data Exploration Method for Large Data Sets and Clustering -- Explorative Data Mining on Stock Data ? Experimental Results and Findings -- Graph Structural Mining in Terrorist Networks -- Characterizing Pseudobase and Predicting RNA Secondary Structure with Simple H-Type Pseudoknots Based on Dynamic Programming -- Locally Discriminant Projection with Kernels for Feature Extraction -- A GA-Based Feature Subset Selection and Parameter Optimization of Support Vector Machine for Content ? Based Image Retrieval -- E-Stream: Evolution-Based Technique for Stream Clustering -- H-BayesClust: A New Hierarchical Clustering Based on Bayesian Networks -- An Improved AdaBoost Algorithm Based on Adaptive Weight Adjusting. 330 $aThe Third International Conference on Advanced Data Mining and Applications (ADMA) organized in Harbin, China continued the tradition already established by the first two ADMA conferences in Wuhan in 2005 and Xi?an in 2006. One major goal of ADMA is to create a respectable identity in the data mining research com- nity. This feat has been partially achieved in a very short time despite the young age of the conference, thanks to the rigorous review process insisted upon, the outstanding list of internationally renowned keynote speakers and the excellent program each year. The impact of a conference is measured by the citations the conference papers receive. Some have used this measure to rank conferences. For example, the independent source cs-conference-ranking.org ranks ADMA (0.65) higher than PAKDD (0.64) and PKDD (0.62) as of June 2007, which are well established conferences in data mining. While the ranking itself is questionable because the exact procedure is not disclosed, it is nevertheless an encouraging indicator of recognition for a very young conference such as ADMA. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v4632 606 $aDatabase management 606 $aArtificial intelligence 606 $aData mining 606 $aSoftware engineering 606 $aInformation technology$xManagement 606 $aApplication software 606 $aDatabase Management 606 $aArtificial Intelligence 606 $aData Mining and Knowledge Discovery 606 $aSoftware Engineering 606 $aComputer Application in Administrative Data Processing 606 $aComputer and Information Systems Applications 615 0$aDatabase management. 615 0$aArtificial intelligence. 615 0$aData mining. 615 0$aSoftware engineering. 615 0$aInformation technology$xManagement. 615 0$aApplication software. 615 14$aDatabase Management. 615 24$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aSoftware Engineering. 615 24$aComputer Application in Administrative Data Processing. 615 24$aComputer and Information Systems Applications. 676 $a005.74 702 $aAlhajj$b Reda 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484309003321 996 $aAdvanced Data Mining and Applications$92982700 997 $aUNINA