000 03746nam a22005535i 4500
001 978-3-642-30278-7
003 DE-He213
005 20200421112039.0
007 cr nn 008mamaa
008 120828s2013 gw | s |||| 0|eng d
020 _a9783642302787
_9978-3-642-30278-7
024 7 _a10.1007/978-3-642-30278-7
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aTowards Advanced Data Analysis by Combining Soft Computing and Statistics
_h[electronic resource] /
_cedited by Christian Borgelt, Mar�ia �Angeles Gil, Jo�ao M.C. Sousa, Michel Verleysen.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aX, 378 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Fuzziness and Soft Computing,
_x1434-9922 ;
_v285
505 0 _aFrom the Contents: Arithmetic and Distance-Based Approach to the Statistical Analysis of Imprecisely Valued Data -- Linear Regression Analysis for Interval-valued Data Based on Set Arithmetic: A Bootstrap Confidence Intervals for the Parameters of a Linear Regression Model with Fuzzy Random Variables -- On the Estimation of the Regression Model M for Interval Data -- Hybrid Least-Squares Regression Modelling Using Confidence -- Testing the Variability of Interval Data: An Application to Tidal Fluctuation.-Comparing the Medians of a Random Interval Defined by Means of Two Different L1 Metrics.-Comparing the Representativeness of the 1-norm Median for Likert and Free-response Fuzzy Scales.-Fuzzy Probability Distributions in Reliability Analysis, Fuzzy HPD-regions, and Fuzzy Predictive Distributions.
520 _aSoft computing, as an engineering science, and statistics, as a classical branch of mathematics, emphasize different aspects of data analysis. Soft computing focuses on obtaining working solutions quickly, accepting approximations and unconventional approaches. Its strength lies in its flexibility to create models that suit the needs arising in applications. In addition, it emphasizes the need for intuitive and interpretable models, which are tolerant to imprecision and uncertainty. Statistics is more rigorous and focuses on establishing objective conclusions based on experimental data by analyzing the possible situations and their (relative) likelihood. It emphasizes the need for mathematical methods and tools to assess solutions and guarantee performance. Combining the two fields enhances the robustness and generalizability of data analysis methods, while preserving the flexibility to solve real-world problems efficiently and intuitively.
650 0 _aEngineering.
650 0 _aMathematical statistics.
650 0 _aData mining.
650 0 _aComputer simulation.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aSimulation and Modeling.
700 1 _aBorgelt, Christian.
_eeditor.
700 1 _aGil, Mar�ia �Angeles.
_eeditor.
700 1 _aSousa, Jo�ao M.C.
_eeditor.
700 1 _aVerleysen, Michel.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642302770
830 0 _aStudies in Fuzziness and Soft Computing,
_x1434-9922 ;
_v285
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-30278-7
912 _aZDB-2-ENG
942 _cEBK
999 _c56513
_d56513