000 | 05418nam a22006135i 4500 | ||
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001 | 978-3-642-41398-8 | ||
003 | DE-He213 | ||
005 | 20200421112037.0 | ||
007 | cr nn 008mamaa | ||
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020 |
_a9783642413988 _9978-3-642-41398-8 |
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024 | 7 |
_a10.1007/978-3-642-41398-8 _2doi |
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_aUN _2bicssc |
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_a005.74 _223 |
245 | 1 | 0 |
_aAdvances in Intelligent Data Analysis XII _h[electronic resource] : _b12th International Symposium, IDA 2013, London, UK, October 17-19, 2013. Proceedings / _cedited by Allan Tucker, Frank H�oppner, Arno Siebes, Stephen Swift. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013. |
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300 |
_aXIV, 464 p. 140 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aLecture Notes in Computer Science, _x0302-9743 ; _v8207 |
|
505 | 0 | _aData, Not Dogma: Big Data, Open Data, and the Opportunities Ahead -- Computational Techniques for Crop Disease Monitoring in the Developing World -- Subjective Interestingness in Exploratory Data Mining -- Time Point Estimation of a Single Sample from High Throughput Experiments Based on Time-Resolved Data and Robust Correlation Measures -- Detecting Events in Molecular Dynamics Simulations -- Graph Clustering by Maximizing Statistical Association Measures -- Evaluation of Association Rule Quality Measures through Feature Extraction -- Towards Comprehensive Concept Description Based on Association Rules -- CD-MOA: Change Detection Framework for Massive Online Analysis -- Integrating Multiple Studies of Wheat Microarray Data to Identify Treatment-Specific Regulatory Networks -- Finding Frequent Patterns in Parallel Point Processes -- Behavioral Clustering for Point Processes -- Estimating Prediction Certainty in Decision Trees -- Interactive Discovery of Interesting Subgroup Sets -- Gaussian Mixture Models for Time Series Modeling, Forecasting, and Interpolation -- When Does Active Learning Work -- Order Span: Mining Closed Partially Ordered Patterns -- Learning Multiple Temporal Matching for Time Series Classification -- On the Importance of Nonlinear Modeling in Computer Performance Prediction -- Diversity-Driven Widening -- Towards Indexing of Web3D Signing Avatars -- Variational Bayesian PCA versus k-NN on a Very Sparse Reddit Voting Dataset -- Analysis of Cluster Structure in Large-Scale English Wikipedia Category Networks -- 1d-SAX: A Novel Symbolic Representation for Time Series -- Learning Models of Activities Involving Interacting Objects -- Correcting the Usage of the Hoeffding Inequality in Stream Mining -- Exploratory Data Analysis through the Inspection of the Probability Density Function of the Number of Neighbors -- The Modeling of Glaucoma Progression through the Use of Cellular Automata -- Towards Narrative Ideation via Cross-Context Link Discovery Using Banded Matrices -- Gaussian Topographic Co-clustering Model -- Preventing Churn in Telecommunications: The Forgotten Network -- Computational Properties of Fiction Writing and Collaborative Work -- Classifier Evaluation with Missing Negative Class Labels -- Dynamic MMHC: A Local Search Algorithm for Dynamic Bayesian Network Structure Learning -- Accurate Visual Features for Automatic Tag Correction in Videos -- Ontology Database System and Triggers -- A Policy Iteration Algorithm for Learning from Preference-Based Feedback -- Multiclass Learning from Multiple Uncertain Annotations -- Learning Compositional Hierarchies of a Sensorimotor System. | |
520 | _aThis book constitutes the refereed conference proceedings of the 12th International Conference on Intelligent Data Analysis, which was held in October 2013 in London, UK. The 36 revised full papers together with 3 invited papers were carefully reviewed and selected from 84 submissions handling all kinds of modeling and analysis methods, irrespective of discipline. The papers cover all aspects of intelligent data analysis, including papers on intelligent support for modeling and analyzing data from complex, dynamical systems. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aAlgorithms. | |
650 | 0 | _aDatabase management. | |
650 | 0 | _aData mining. | |
650 | 0 | _aInformation storage and retrieval. | |
650 | 0 | _aArtificial intelligence. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aDatabase Management. |
650 | 2 | 4 | _aInformation Systems Applications (incl. Internet). |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aInformation Storage and Retrieval. |
650 | 2 | 4 | _aAlgorithm Analysis and Problem Complexity. |
650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
700 | 1 |
_aTucker, Allan. _eeditor. |
|
700 | 1 |
_aH�oppner, Frank. _eeditor. |
|
700 | 1 |
_aSiebes, Arno. _eeditor. |
|
700 | 1 |
_aSwift, Stephen. _eeditor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783642413971 |
830 | 0 |
_aLecture Notes in Computer Science, _x0302-9743 ; _v8207 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-642-41398-8 |
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