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UPDATE `aff_pdf_cache` SET `cache` = 'a:10:{i:0;O:8:\"stdClass\":13:{s:2:\"id\";s:6:\"104341\";s:6:\"status\";s:8:\"verified\";s:11:\"author_name\";s:6:\"eliasz\";s:9:\"author_id\";s:1:\"0\";s:14:\"author_website\";s:0:\"\";s:5:\"title\";s:61:\"Machine Learning and Data Mining: 19 Mining Text And Web Data\";s:11:\"description\";s:62:\"Machine Learning and Data Mining: 19 Mining Text And Web Data \";s:5:\"thumb\";s:105:\"data/thumb/Machine-Learning-and-Data-Mining-19-Mining-Text-And-Web-Data-Presentation-Transcript-22456.jpg\";s:6:\"thumb2\";s:106:\"data/thumb2/Machine-Learning-and-Data-Mining-19-Mining-Text-And-Web-Data-Presentation-Transcript-22456.jpg\";s:9:\"permalink\";s:60:\"machine-learning-and-data-mining-19-mining-text-and-web-data\";s:5:\"pages\";s:2:\"75\";s:6:\"rating\";s:1:\"0\";s:5:\"voter\";s:1:\"0\";}i:1;O:8:\"stdClass\":13:{s:2:\"id\";s:4:\"5080\";s:6:\"status\";s:8:\"verified\";s:11:\"author_name\";s:6:\"shinta\";s:9:\"author_id\";s:3:\"377\";s:14:\"author_website\";s:0:\"\";s:5:\"title\";s:105:\"User-Oriented Machine Learning Strategies for Information Extraction : Putting the Human Back in the Loog\";s:11:\"description\";s:1155:\"Efforts in information extraction (IE) have concen-
\ntrated on fundamental issues concerning the viability
\nof the technology. Two of these important issues are
\nscalability and portability. To illustrate, consider the
\nAutoSlog system for learning a dictionary of extrac-
\ntion patterns [Riloff, 1993]. AutoSlog automatically ac-
\nquires extraction patterns based on training documents
\nannotated with extraction templates, thus eliminating
\nthe need for a human programmer to encode patterns
\nor rules for extraction. Even further, more recent ver-
\nsions of AutoSlog acquire these patterns without explicit
\ntraining data, using either relevancy signatures [Riloff,
\n1996] or mutual bootstrapping with a semantic lexicon
\n[Riloff and Jones, 1999], eliminating the need for a hu-
\nman annotator to produce training data from raw docu-
\nments. In short, to address the issues of scalability and
\nportability, it has been important to find ways to get the
\nhuman out of the loop, both as a programmer encoding
\npatterns, and as an annotator producing training data.\";s:5:\"thumb\";s:119:\"images/t/51/user-oriented-machine-learning-strategies-for-information-extraction-putting-the-human-back-in-the-loog.jpg\";s:6:\"thumb2\";s:120:\"images/t2/51/user-oriented-machine-learning-strategies-for-information-extraction-putting-the-human-back-in-the-loog.jpg\";s:9:\"permalink\";s:103:\"user-oriented-machine-learning-strategies-for-information-extraction-putting-the-human-back-in-the-loog\";s:5:\"pages\";s:1:\"2\";s:6:\"rating\";s:1:\"0\";s:5:\"voter\";s:1:\"0\";}i:2;O:8:\"stdClass\":13:{s:2:\"id\";s:6:\"105274\";s:6:\"status\";s:8:\"verified\";s:11:\"author_name\";s:6:\"isabel\";s:9:\"author_id\";s:1:\"0\";s:14:\"author_website\";s:0:\"\";s:5:\"title\";s:21:\"Machine Learning in R\";s:11:\"description\";s:22:\"Machine Learning in R \";s:5:\"thumb\";s:66:\"data/thumb/Machine-Learning-in-R-Presentation-Transcript-26093.jpg\";s:6:\"thumb2\";s:67:\"data/thumb2/Machine-Learning-in-R-Presentation-Transcript-26093.jpg\";s:9:\"permalink\";s:21:\"machine-learning-in-r\";s:5:\"pages\";s:3:\"151\";s:6:\"rating\";s:1:\"0\";s:5:\"voter\";s:1:\"0\";}i:3;O:8:\"stdClass\":13:{s:2:\"id\";s:4:\"5118\";s:6:\"status\";s:8:\"verified\";s:11:\"author_name\";s:6:\"shinta\";s:9:\"author_id\";s:3:\"377\";s:14:\"author_website\";s:0:\"\";s:5:\"title\";s:58:\"Traffic Accident Analysis Using Machine Learning Paradigms\";s:11:\"description\";s:1252:\"Engineers and researchers in the automobile industry have tried to design and build safer automobiles,
\nbut traffic accidents are unavoidable. Patterns involved in dangerous crashes could be detected if we
\ndevelop accurate prediction models capable of automatic classification of type of injury severity of
\nvarious traffic accidents. These behavioral and roadway accident patterns can be useful to develop
\ntraffic safety control policies. We believe that to obtain the greatest possible accident reduction effects
\nwith limited budgetary resources, it is important that measures be based on scientific and objective
\nsurveys of the causes of accidents and severity of injuries. This paper summarizes the performance of
\nfour machine learning paradigms applied to modeling the severity of injury that occurred during traffic
\naccidents. We considered neural networks trained using hybrid learning approaches, support vector
\nmachines, decision trees and a concurrent hybrid model involving decision trees and neural networks.
\nExperiment results reveal that among the machine learning paradigms considered the hybrid decision
\ntree-neural network approach outperformed the individual approaches.\";s:5:\"thumb\";s:74:\"images/t/52/traffic-accident-analysis-using-machine-learning-paradigms.jpg\";s:6:\"thumb2\";s:75:\"images/t2/52/traffic-accident-analysis-using-machine-learning-paradigms.jpg\";s:9:\"permalink\";s:58:\"traffic-accident-analysis-using-machine-learning-paradigms\";s:5:\"pages\";s:2:\"10\";s:6:\"rating\";s:3:\"2.5\";s:5:\"voter\";s:1:\"2\";}i:4;O:8:\"stdClass\":13:{s:2:\"id\";s:6:\"421238\";s:6:\"status\";s:8:\"verified\";s:11:\"author_name\";s:6:\"ijcatr\";s:9:\"author_id\";s:1:\"0\";s:14:\"author_website\";s:0:\"\";s:5:\"title\";s:63:\"A Review of Machine Learning based Anomaly Detection Techniques\";s:11:\"description\";s:380:\"Intrusion detection is so much popular since the last two decades where intrusion is attempted to break into or misuse\nthe system. It is mainly of two types based on the intrusions, first is Misuse or signature based detection and the other is Anomaly\ndetection. In this paper Machine learning based methods which are one of the types of Anomaly detection techniques is\ndiscussed.\";s:5:\"thumb\";s:81:\"images/t/4213/a-review-of-machine-learning-based-anomaly-detection-techniques.jpg\";s:6:\"thumb2\";s:82:\"images/t2/4213/a-review-of-machine-learning-based-anomaly-detection-techniques.jpg\";s:9:\"permalink\";s:63:\"a-review-of-machine-learning-based-anomaly-detection-techniques\";s:5:\"pages\";s:1:\"3\";s:6:\"rating\";s:1:\"0\";s:5:\"voter\";s:1:\"0\";}i:5;O:8:\"stdClass\":13:{s:2:\"id\";s:5:\"12235\";s:6:\"status\";s:8:\"verified\";s:11:\"author_name\";s:7:\"samanta\";s:9:\"author_id\";s:4:\"1916\";s:14:\"author_website\";s:0:\"\";s:5:\"title\";s:99:\"Focusing on the relation: fewer exemplars facilitate children\'s initial verb learning and extension\";s:11:\"description\";s:949:\"One of the most prominent theories for why children struggle to learn verbs is that verb learning requires the abstraction of relations between an object and its action (Gentner, 2003). Two hypotheses suggest how children extract relations to extend a novel verb: (1) seeing many different exemplars allows children to detect the invariant relation between actions in different contexts (Gentner, 2003), and (2) repetition of fewer exemplars allows children to move beyond the entities involved to extract the relation (Kersten & Smith, 2002). We tested - and 3-year-olds\' ability to extend a novel verb after viewing the repetition of one novel actor compared to four different actors performing a novel action. Both ages were better at learning and extending a novel verb to a novel actor when shown only one actor rather than four different actors. These results indicate that during initial verb learning less information is more effective.\";s:5:\"thumb\";s:115:\"images/t/123/focusing-on-the-relation-fewer-exemplars-facilitate-children-s-initial-verb-learning-and-extension.jpg\";s:6:\"thumb2\";s:116:\"images/t2/123/focusing-on-the-relation-fewer-exemplars-facilitate-children-s-initial-verb-learning-and-extension.jpg\";s:9:\"permalink\";s:98:\"focusing-on-the-relation-fewer-exemplars-facilitate-children-s-initial-verb-learning-and-extension\";s:5:\"pages\";s:1:\"7\";s:6:\"rating\";s:1:\"0\";s:5:\"voter\";s:1:\"0\";}i:6;O:8:\"stdClass\":13:{s:2:\"id\";s:5:\"96113\";s:6:\"status\";s:8:\"verified\";s:11:\"author_name\";s:5:\"holly\";s:9:\"author_id\";s:1:\"0\";s:14:\"author_website\";s:0:\"\";s:5:\"title\";s:26:\"Practical Machine Learning\";s:11:\"description\";s:28:\"Practical Machine Learning \";s:5:\"thumb\";s:70:\"data/thumb/Practical-Machine-Learning-Presentation-Transcript-1024.jpg\";s:6:\"thumb2\";s:71:\"data/thumb2/Practical-Machine-Learning-Presentation-Transcript-1024.jpg\";s:9:\"permalink\";s:26:\"practical-machine-learning\";s:5:\"pages\";s:2:\"33\";s:6:\"rating\";s:1:\"0\";s:5:\"voter\";s:1:\"0\";}i:7;O:8:\"stdClass\":13:{s:2:\"id\";s:6:\"108046\";s:6:\"status\";s:8:\"verified\";s:11:\"author_name\";s:4:\"nayu\";s:9:\"author_id\";s:1:\"0\";s:14:\"author_website\";s:0:\"\";s:5:\"title\";s:26:\"Machine learning Lecture 2\";s:11:\"description\";s:27:\"Machine learning Lecture 2 \";s:5:\"thumb\";s:71:\"data/thumb/Machine-learning-Lecture-2-Presentation-Transcript-35984.jpg\";s:6:\"thumb2\";s:72:\"data/thumb2/Machine-learning-Lecture-2-Presentation-Transcript-35984.jpg\";s:9:\"permalink\";s:26:\"machine-learning-lecture-2\";s:5:\"pages\";s:2:\"86\";s:6:\"rating\";s:1:\"0\";s:5:\"voter\";s:1:\"0\";}i:8;O:8:\"stdClass\":13:{s:2:\"id\";s:6:\"121807\";s:6:\"status\";s:8:\"verified\";s:11:\"author_name\";s:7:\"atsushi\";s:9:\"author_id\";s:1:\"0\";s:14:\"author_website\";s:0:\"\";s:5:\"title\";s:53:\"Project Management Process Groups And Knowledge Areas\";s:11:\"description\";s:275:\"\n

  1. Project Management Process Groups and Knowledge Areas Source: PMBOK 4 th Edition Prepared By: [email_address]
  2. Initiating Planning Executing Monitoring & Control Closing 4.Integration 5.Scope 6.Time 7.Cost 8.Quality 9.HR 10.Communications 11.Risk…\";s:5:\"thumb\";s:98:\"data/thumb/Project-Management-Process-Groups-And-Knowledge-Areas-Presentation-Transcript-19032.jpg\";s:6:\"thumb2\";s:99:\"data/thumb2/Project-Management-Process-Groups-And-Knowledge-Areas-Presentation-Transcript-19032.jpg\";s:9:\"permalink\";s:53:\"project-management-process-groups-and-knowledge-areas\";s:5:\"pages\";s:2:\"39\";s:6:\"rating\";s:1:\"0\";s:5:\"voter\";s:1:\"0\";}i:9;O:8:\"stdClass\":13:{s:2:\"id\";s:6:\"127124\";s:6:\"status\";s:8:\"verified\";s:11:\"author_name\";s:6:\"ishaan\";s:9:\"author_id\";s:1:\"0\";s:14:\"author_website\";s:0:\"\";s:5:\"title\";s:75:\"Introduction to Personal Learning and Thinking Skills and Functional Skills\";s:11:\"description\";s:224:\"\n
    1. Functional and Personal Learning and Thinking Skills - supporting learning John Pallister 26 th January 2009
    2. By the end of the session you will:
      • know what Functional Skills and Personal…\";s:5:\"thumb\";s:120:\"data/thumb/Introduction-to-Personal-Learning-and-Thinking-Skills-and-Functional-Skills-Presentation-Transcript-37452.jpg\";s:6:\"thumb2\";s:121:\"data/thumb2/Introduction-to-Personal-Learning-and-Thinking-Skills-and-Functional-Skills-Presentation-Transcript-37452.jpg\";s:9:\"permalink\";s:75:\"introduction-to-personal-learning-and-thinking-skills-and-functional-skills\";s:5:\"pages\";s:2:\"28\";s:6:\"rating\";s:1:\"0\";s:5:\"voter\";s:1:\"0\";}}', `cache_on` = '2015-02-28 14:18:15' WHERE `aff_id` = '442386'