LEADER 04667nas# 22004691i 450 001 SUN0036340 005 20110914114931.414 011 $a1120-3781 100 $a20050518b19831999 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| ||||| 110 $aaT||||||||| 200 1 $aProspettive psicoanalitiche nel lavoro istituzionale 207 $aRoma : Il pensiero scientifico, 1983-1999 210 $d17 v. ; 24 cm 215 $aSemestrale, quadrimestrale dal 1989. 620 $dRoma$3SUNL000360 676 $a616.89$cDISTURBI MENTALI$v21 712 $aIl pensiero scientifico$3SUNV000063$4650 801 $aIT$bSOL$c20181109$gRICA 899 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$1IT-CE0119$2SUN16$41983; 1985-1999; lac. 1997;$cI-4E.1 ;$nNA229 912 $aSUN0036340 950 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$b16 1983; 1985-1999; lac. 1997;$d16 PERIO I-4E.1 1983 $e16 9710 provvisorio$d16 PERIO I-4E.1 1985 $e16 9711 $d16 PERIO I-4E.1 1986 $e16 9712 provvisorio$d16 PERIO I-4E.1 1987 $e16 9713 provvisorio$d16 PERIO I-4E.1 1988 $e16 9714 provvisorio$d16 PERIO I-4E.1 1989 $e16 9715 provvisorio$d16 PERIO I-4E.1 1990 $e16 9716 provvisorio$d16 PERIO I-4E.1 1991 $e16 9717 provvisorio$d16 PERIO I-4E.1 1992 $e16 9718 provvisorio$d16 PERIO I-4E.1 1993 $e16 9719 provvisorio$d16 PERIO I-4E.1 1994 $e16 9720 provvisorio$d16 PERIO I-4E.1 1995 $e16 9721 provvisorio$d16 PERIO I-4E.1 1996 $e16 LET4972 $d16 PERIO I-4E.1 1997 $e16 LET5128 $d16 PERIO I-4E.1 1998 $e16 LET6461 $d16 PERIO I-4E.1 1999 $e16 LET7855 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$h9710$kPERIO I-4E.1 1983$op$pp$qa$uprovvisorio 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$h9711$kPERIO I-4E.1 1985$op$pp$qa 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$h9712$kPERIO I-4E.1 1986$op$pp$qa$uprovvisorio 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$h9713$kPERIO I-4E.1 1987$op$pp$qa$uprovvisorio 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$h9714$kPERIO I-4E.1 1988$op$pp$qa$uprovvisorio 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$h9715$kPERIO I-4E.1 1989$op$pp$qa$uprovvisorio 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$h9716$kPERIO I-4E.1 1990$op$pp$qa$uprovvisorio 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$h9717$kPERIO I-4E.1 1991$op$pp$qa$uprovvisorio 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$h9718$kPERIO I-4E.1 1992$op$pp$qa$uprovvisorio 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$h9719$kPERIO I-4E.1 1993$op$pp$qa$uprovvisorio 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$h9720$kPERIO I-4E.1 1994$op$pp$qa$uprovvisorio 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$h9721$kPERIO I-4E.1 1995$op$pp$qa$uprovvisorio 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$gLET$h4972$kPERIO I-4E.1 1996$oc$pp$qa 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$gLET$h5128$kPERIO I-4E.1 1997$oc$pp$qa 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$gLET$h6461$kPERIO I-4E.1 1998$oc$pp$qa 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$gLET$h7855$kPERIO I-4E.1 1999$oc$pp$qa 996 $aProspettive psicoanalitiche nel lavoro istituzionale$9791041 997 $aUNICAMPANIA LEADER 09454nam 22008175 450 001 9910483389103321 005 20251226193451.0 010 $a3-319-17876-8 024 7 $a10.1007/978-3-319-17876-9 035 $a(CKB)3710000000404083 035 $a(SSID)ssj0001501626 035 $a(PQKBManifestationID)11854472 035 $a(PQKBTitleCode)TC0001501626 035 $a(PQKBWorkID)11446474 035 $a(PQKB)11267656 035 $a(DE-He213)978-3-319-17876-9 035 $a(MiAaPQ)EBC6295267 035 $a(MiAaPQ)EBC5591532 035 $a(Au-PeEL)EBL5591532 035 $a(OCoLC)908560111 035 $a(PPN)18548915X 035 $a(EXLCZ)993710000000404083 100 $a20150427d2015 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aNew Frontiers in Mining Complex Patterns $eThird International Workshop, NFMCP 2014, Held in Conjunction with ECML-PKDD 2014, Nancy, France, September 19, 2014, Revised Selected Papers /$fedited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (XII, 211 p. 61 illus.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v8983 300 $aIncludes index. 311 08$a3-319-17875-X 327 $aIntro -- Preface -- Organization -- Sampling and Presenting Patterns from Structured Data -- Contents -- Classification and Regression -- Semi-supervised Learning for Multi-target Regression -- 1 Introduction -- 2 MTR with Ensembles of Predictive Clustering Trees -- 2.1 Predictive Clustering Trees for MTR -- 2.2 Ensembles of PCTs -- 3 Self-training for MTR with Ensembles of PCTs -- 4 Experimental Design -- 4.1 Data Description -- 4.2 Experimental Setup and Evaluation Procedure -- 5 Results and Discussion -- 6 Conclusions -- References -- Evaluation of Different Data-Derived Label Hierarchies in Multi-label Classification -- 1 Introduction -- 2 Background -- 2.1 The Task of Multi-label Classification (MLC) -- 2.2 The Task of Hierarchical Multi-label Classification (HMC) -- 3 The Use of Data Derived Label Hierarchies in Multi-Label Classification -- 3.1 Generating a Label Hierarchy on a Multi-label Output Space -- 3.2 Solving MLC Problems by Using Classification Approaches for HMC -- 3.3 Classification Approaches for HMC -- 4 Experimental Design -- 4.1 Datasets and Evaluation Measures -- 4.2 Experimental Setup -- 4.3 Statistical Evaluation -- 5 Results and Discussion -- 6 Conclusions and Further Work -- A Evaluation Measures -- A.1 Example Based Measures -- A.2 Label Based Measures -- A.3 Ranking Based Measures -- B Complete Results from the Experimental Evaluation -- References -- Clustering -- Predicting Negative Side Effects of Surgeries Through Clustering -- 1 Introduction -- 2 Background -- 2.1 Multi-valued Information System -- 2.2 Atomic Action Terms and Action Terms -- 2.3 Meta-Actions for Multi-valued Information System -- 3 Negative Side Effects -- 4 Clustering Based on Negative Side Effects -- 5 New Approach for Predicting Negative Side Effects -- 5.1 Distance Between Two Patients -- 5.2 Distance Between a Patient and a Cluster. 327 $a6 Dataset and Experiments -- 6.1 HCUP Dataset Description -- 6.2 Experiments -- 7 Summary and Conclusions -- References -- Parallel Multicut Segmentation via Dual Decomposition -- 1 Introduction -- 2 Related Work -- 3 Segmentations and Multicuts -- 4 Outer Relaxation of the Cycle Polytope -- 5 Lagrangian Decomposition -- 5.1 Constrained Reparameterization -- 6 Bound Maximization Along Subgradients -- 7 Rounding Heuristic and Interpretation -- 7.1 Decoding Heuristic: Iterative Construction -- 8 Experiments -- 8.1 Berkeley Segmentation Data Set -- 8.2 Correlation Clustering in Non-planar Graphs -- 9 Discussion -- References -- Learning from Imbalanced Data Using Ensemble Methods and Cluster-Based Undersampling -- 1 Introduction -- 2 Related Work -- 3 Proposed Algorithms -- 3.1 Undersampling Based on Clustering and K-Nearest Neighbour -- 3.2 Undersampling Based on Clustering and Ensemble Learning -- 4 Experiments and Results -- 4.1 Evaluation Criteria -- 4.2 Datasets and Experimental Settings -- 4.3 Results and Analyses -- 5 Conclusion and Future Work -- References -- Data Streams and Sequences -- Prequential AUC for Classifier Evaluation and Drift Detection in Evolving Data Streams -- 1 Introduction -- 2 Evaluating Data Stream Classifiers -- 3 Prequential AUC -- 4 Drift Detection Using AUC -- 5 Experiments -- 5.1 Datasets -- 5.2 Results -- 6 Conclusions -- References -- Mining Positional Data Streams -- 1 Introduction -- 2 Related Work -- 3 Efficiently Finding Similar Movements -- 3.1 Representation -- 3.2 Approximate Dynamic Time Warping -- 3.3 An N-Best Algorithm -- 3.4 Distance-Based Hashing -- 4 Frequent Episode Mining for Positional Data -- 4.1 Counting Phase -- 4.2 Generation Phase -- 5 Empirical Evaluation -- 5.1 Positional Data -- 5.2 Near Neighbour Search -- 5.3 Episode Discovery -- 6 Conclusion -- References. 327 $aVisualization for Streaming Telecommunications Networks -- 1 Motivation -- 2 Related Work -- 2.1 Visualization -- 2.2 top-k Itemsets -- 3 Streaming Simulation System -- 3.1 Components -- 3.2 Landmark Windows -- 3.3 Sliding Windows -- 3.4 top-k Networks -- 4 Case Study -- 4.1 Data Description -- 4.2 Sliding Windows Visualization -- 4.3 top-k Landmark Window -- 5 Conclusions -- References -- Temporal Dependency Detection Between Interval-Based Event Sequences -- 1 Introduction -- 2 Temporal Dependencies -- 2.1 Temporal Dependency Assessment -- 2.2 Significant Temporal Dependencies Selection -- 3 Discovery of Temporal Dependencies -- 4 Experimental Study -- 4.1 Quantitative Experiments -- 4.2 Case Study -- 5 Related Work -- 6 Conclusion -- References -- Applications -- Discovering Behavioural Patterns in Knowledge-Intensive Collaborative Processes -- 1 Introduction -- 1.1 Motivation -- 2 Related Work -- 3 Behavioural Pattern -- 4 Methodology -- 4.1 Case Study: Collaborative Research Activity -- 4.2 Log Building -- 4.3 Hierarchical Clustering -- 5 Experiments -- 5.1 Discussion -- 6 Conclusions and Future Work -- References -- Learning Complex Activity Preconditions in Process Mining -- 1 Introduction -- 2 Representation -- 3 Learning -- 4 Computational Complexity Issues -- 5 Exploitation -- 6 Evaluation -- 7 Conclusions -- References -- Location Prediction of Mobile Phone Users Using Apriori-Based Sequence Mining with Multiple Support Thresholds -- 1 Introduction -- 2 Previous Work -- 3 Proposed Technique -- 3.1 Preliminaries -- 3.2 Apriori-Based Sequence Mining Algorithm with Multiple Support Thresholds (ASMAMS) -- 4 Evaluation -- 5 Experimental Results -- 6 Conclusion -- References -- Pitch-Related Identification of Instruments in Classical Music Recordings -- 1 Introduction -- 2 Audio Data -- 2.1 Parametrization -- 3 Classification with Random Forests. 327 $a3.1 Instrument and Pitch Identification -- 3.2 Cleaning -- 3.3 Training of Random Forests -- 4 Results -- 5 Summary and Conclusions -- References -- Author Index. 330 $aThis book constitutes the thoroughly refereed post-conference proceedings of the Third International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2014, held in conjunction with ECML-PKDD 2014 in Nancy, France, in September 2014. The 13 revised full papers presented were carefully reviewed and selected from numerous submissions. They illustrate advanced data mining techniques which preserve the informative richness of complex data and allow for efficient and effective identification of complex information units present in such data. The papers are organized in the following sections: classification and regression; clustering; data streams and sequences; applications. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v8983 606 $aData mining 606 $aDatabase management 606 $aInformation storage and retrieval systems 606 $aArtificial intelligence 606 $aData Mining and Knowledge Discovery 606 $aDatabase Management 606 $aInformation Storage and Retrieval 606 $aArtificial Intelligence 615 0$aData mining. 615 0$aDatabase management. 615 0$aInformation storage and retrieval systems. 615 0$aArtificial intelligence. 615 14$aData Mining and Knowledge Discovery. 615 24$aDatabase Management. 615 24$aInformation Storage and Retrieval. 615 24$aArtificial Intelligence. 676 $a006.3 702 $aAppice$b Annalisa$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCeci$b Michelangelo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLoglisci$b Corrado$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aManco$b Giuseppe$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMasciari$b Elio$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRas$b Zbigniew W$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483389103321 996 $aNew Frontiers in Mining Complex Patterns$92177052 997 $aUNINA