http://www.bibsonomy.org/burst/user/hotho/datasetBibSonomy BuRST Feed for /user/hotho/dataset2008-07-26T21:24:22+02:00Semantic feature production norms for a large set ...[Behav Res Methods. 2005] - PubMed Resulthttp://www.bibsonomy.org/bibtex/2936af12b025e37b0a6aac6bc103f58a3/hothohotho2008-05-08T12:17:01+02:00semantic toread relation grounding ontology ol dataset <span style="color:#555555;">K <a href="http://www.bibsonomy.org/author/McRae">McRae</a> and G S <a href="http://www.bibsonomy.org/author/Cree">Cree</a> and M S <a href="http://www.bibsonomy.org/author/Seidenberg">Seidenberg</a> and C <a href="http://www.bibsonomy.org/author/McNorgan">McNorgan</a> </span><em>Behav Res Methods</em><em>37(4):547-559</em><em>Nov2005. </em>Thu May 08 12:17:01 CEST 2008Behav Res MethodsNov4547-559Semantic feature production norms for a large set of living and nonliving things372005semantic toread relation grounding ontology ol dataset Semantic features have provided insight into numerous behavioral phenomena concerning concepts, categorization, and semantic memory in adults, children, and neuropsychological populations. Numerous theories and models in these areas are based on representations and computations involving semantic features. Consequently, empirically derived semantic feature production norms have played, and continue to play, a highly useful role in these domains. This article describes a set of feature norms collected from approximately 725 participants for 541 living (dog) and nonliving (chair) basic-level concepts, the largest such set of norms developed to date. This article describes the norms and numerous statistics associated with them. Our aim is to make these norms available to facilitate other research, while obviating the need to repeat the labor-intensive methods involved in collecting and analyzing such norms. The full set of norms may be downloaded from www.psychonomic.org/archive.[cs/0610105] How To Break Anonymity of the Netflix Prize Datasethttp://www.bibsonomy.org/bibtex/286b686a7fad55fa225123b2f79de87a8/hothohotho2007-12-14T09:04:20+01:00anonymity Preis prize dataset netflix recommender <span style="color:#555555;">Arvind <a href="http://www.bibsonomy.org/author/Narayanan">Narayanan</a> and Vitaly <a href="http://www.bibsonomy.org/author/Shmatikov">Shmatikov</a> </span>(<em>2006</em>)Fri Dec 14 09:04:20 CET 2007How To Break Anonymity of the Netflix Prize Dataset2006anonymity Preis prize dataset netflix recommender We present a new class of statistical de-anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on. Our techniques are robust to perturbation in the data and tolerate some mistakes in the adversary's background knowledge.UCI Machine Learning Repositoryhttp://www.bibsonomy.org/bibtex/285308db3df761f63f16a7cab4eb8d4aa/hothohotho2006-06-23T07:06:19+02:00ml dm mining dataset data uci machine learning <span style="color:#555555;">C.L. <a href="http://www.bibsonomy.org/author/Blake D.J. Newman">Blake D.J. Newman</a> and C.J. <a href="http://www.bibsonomy.org/author/Merz">Merz</a> </span>(<em>1998</em>)Fri Jun 23 07:06:19 CEST 2006{UCI} Repository of machine learning databases1998ml dm mining dataset data uci machine learning