Kooperativer Bibliotheksverbund

UID:

Format: 782 S.

Edition: 1. Aufl.

ISBN: 9783958900103

Content: Eine Jugend im Nationalsozialismus

Language: German

URL: Inhaltsverzeichnis

UID:

Format: 320 S.

ISBN: 9783746634135

Series Statement: Band 1

Content: Eigentlich wollte Frieke nur kurz auf Spiekeroog bleiben. Doch dann will ihr Vater, dem sie seit Jahrzehnten erfolgreich aus dem Weg geht, plötzlich an ihrem Leben teilhaben. Der Forscher, den sie über eine seltene Vogelart interviewen soll, entpuppt sich als äußerst charmant, und in der Inselbuchhandlung erinnert sie sich an ihren längst vergessenen Lebenstraum: Menschen mit Büchern glücklich zu machen.

Language: German

Keywords: Fiktionale Darstellung

URL: http://deposit.dnb.de/cgi-bin/dokserv?id=c4575e9a97964df98362135ca1297516&prov=M&dok_var=1&dok_ext=htm

UID:

Format: 1 online resource (255 pages)

Edition: 1st ed.

ISBN: 9781119097914

Series Statement: AD Smart Series

Note: Intro -- COMPUTING THE ENVIRONMENT -- CONTENTS -- FOREWORD: COMPUTING THE ENVIRONMENT -- 1. INTRODUCTION-COMPUTING THE ENVIRONMENT: DESIGN WORKFLOWS FOR THE SIMULATION OF SUSTAINABLE ARCHITECTURE -- DATA, DRAWING AND SIMULATION -- COMPUTATION IN PRACTICE -- A DEEPER WAY TO THINK -- ENVIRONMENTAL IMPACTS AND THE HUMAN DIMENSION -- THE STRUCTURE OF THE BOOK -- NEW POTENTIALS FOR ARCHITECTURE -- REFERENCES -- IMAGES -- 2. NEW DIALOGUES ABOUT ENERGY: PERFORMANCE, CARBON AND CLIMATE -- PERFORMANCE-BASED DESIGN -- RETHINKING ENERGY -- PREDICTING ENERGY USE -- SO WHY DON'T DESIGNERS MODEL ENERGY? -- VISUALISING ENERGY, CLIMATE AND CARBON -- FUTURE CHALLENGES: SCALE, SKILLS AND ACCURACY -- REFERENCES -- IMAGES -- 3. PARAMETRIC ENVIRONMENTAL DESIGN: SIMULATION AND GENERATIVE PROCESSES -- TOOL MAKERS AND TOOL USERS -- INTEROPERABILITY-NAVIGATING THE SOFTWARE LANDSCAPE -- MODELLING AND SIMULATION -- NEW PARAMETERS -- BUILDING FORM AND SURFACE -- THE FUTURE OF SIMULATION -- REFERENCES -- IMAGES -- 4. DESIGNING ATMOSPHERES: SIMULATING EXPERIENCE -- DESIGNING ENVIRONMENT AND ATMOSPHERE -- HETEROGENEOUS ENVIRONMENTS -- DESIGN FOR SUN AND LIGHT -- THERMAL COMFORT -- USER EXPERIENCE -- COMPUTING FLUID FLOWS -- ACOUSTIC ATMOSPHERES -- BEYOND DRAWING TOWARDS ATMOSPHERE -- REFERENCES -- IMAGES -- 5. USE DATA: COMPUTING LIFE-CYCLE AND REAL-TIME VISUALISATION -- COMPARING AND SHARING DATA -- USING POST-OCCUPANCY DATA -- ECOMETRICS -- CARBON CALCULATOR -- TALLY: CALCULATING AT THE SPEED OF DESIGN -- CREATING USEFUL INFORMATION FROM REAL-TIME ENVIRONMENTAL DATA -- REAL-TIME MONITORING AND TRACKING OF ENERGY USE -- DASHER -- SIMULATING AND VISUALISING WELLNESS -- USE DATA: COMPUTING LIFE-CYCLE AND REAL-TIME VISUALISATION -- REFERENCES -- IMAGES -- 6. NEAR FUTURE DEVELOPMENTS: ADVANCES IN SIMULATION AND REAL-TIME FEEDBACK -- REAL-TIME FEEDBACK -- THE LIVING , ENHANCING BUILDING DATA ANALYSIS EXPERTISE: CASE JOINED WEWORK -- BUILDINGS=DATA -- LASER SCANNING FOR HIGH ACCURACY BIM -- OCCUPANCY MONITORING FOR BUSINESS INTELLIGENCE -- DATA FOR BUILDING CONSTRUCTION -- REAL-TIME SENSOR DATA FOR ENVIRONMENTAL FEEDBACK -- BUILDING ANALYTICS: STRATEGIES FOR COMPUTING THE ENVIRONMENT -- REFERENCES -- IMAGES -- 19. GLOBAL ENVIRONMENTAL CHALLENGES: TECHNOLOGY DESIGN AND ARCHITECTURAL RESPONSES -- INSIDE, OUTSIDE AND ALL AROUND -- PRECISION, INFORMATION, PREFABRICATION -- THE (SIMPLE) MODEL IN YOUR HEAD -- OUR MODEL OF MODELS -- THE FUTURE IS INTERDISCIPLINARY AND IN-HOUSE -- ITERATIVE PROCESSES -- REFERENCES -- IMAGES -- INDEX -- EULA , SKETCHING WITH PROTOTYPES -- AN ECOSYSTEM OF INFORMATION -- BUILDINGS=DATA: UTILISING BEHAVIOUR AS DESIGN INPUT HARNESSING NEW TECHNOLOGIE -- BUILDING GENERATIVE DESIGN -- GRADIENTS OF PERFORMANCE -- ARCHITECTURE AS METEOROLOGY -- FABPOD: DESIGNING FOR AFFECT -- RETHINKING THE ENVIRONMENT -- REFERENCES -- IMAGES -- 7. DESIGNING ENVIRONMENTS AND SIMULATING EXPERIENCE: FOSTER + PARTNERS SPECIALIST MODELLING GROUP -- F+P AND THE SMG -- DESIGNING THE OCEANWIDE CENTER -- EXPERIENCING THE URBAN ROOM -- DESIGNING HUMAN COMFORT -- OPEN-SOURCE SIMULATION -- MIDDLE GROUND TOOLS -- THE SOUND OF SAN FRANCISCO -- DESIGNING EXPERIENCE -- REFERENCES -- IMAGES -- 8. MAXIMISING IMPACT THROUGH PERFORMANCE SIMULATION: THE WORK OF TRANSSOLAR KLIMAENGINEERING -- COMFORTABLE CLOUDSCAPES -- HIGH COMFORT, LOW IMPACT: BREATHE.AUSTRIA -- EXTREME CLIMATE STRATEGIES: MANITOBA HYDRO -- INTERIOR COMFORT: BALTIMORE LAW CENTER -- MATERIAL AND LIFE CYCLE: RICOLA HERB CENTRE -- PASSIVE AND ACTIVE SYSTEMS: SCHOOL DESIGN -- TECHNOLOGY AS AN ALLY FOR GOOD DESIGN -- REFERENCES -- IMAGES -- 9. DESIGNERS NEED FEEDBACK: RESEARCH AND PRACTICE BY KIERANTIMBERLAKE -- TRANSDISCIPLINARY PRACTICE -- DESIGNERS NEED FEEDBACK: INTRODUCING TALLY -- SENSORED ENVIRONMENTS -- POINTELIST AND PERSONAL WEATHER STATIONS -- BESPOKE ENVIRONMENTAL TOOLS -- PREDICTIVE MODELLING IN THE FUTURE -- REFERENCES -- IMAGES -- 10. ARCHITECTURE SHAPES PERFORMANCE: GXN ADVANCES SOLAR MODELLING AND SENSING -- SWEDBANK-DESIGNING FOR DAYLIGHT -- TOWER DESIGNS-PERFORMANCE IMPACTS FORM -- SENSORING ENVIRONMENTS -- FORM ENVIRONMENT BEHAVIOUR -- REFERENCES -- IMAGES -- 11. BESPOKE TOOLS FOR A BETTER WORLD: THE ART OF SUSTAINABLE DESIGN AT BUROHAPPOLD ENGINEERING -- CUSTOM TOOLS FOR SUSTAINABLE DESIGN -- COMPUTING ENERGY USE -- SOLAR GAIN AND DAYLIGHT -- OPTIMISING DESIGN SOLUTIONS -- RESEARCHING TECHNOLOGY FOR SUSTAINABILITY , THE FUTURE: COMFORT, HEALTH AND WELLBEING -- REFERENCES -- IMAGES -- 12. BIG IDEAS: INFORMATION DRIVEN DESIGN -- MUSEUM OF THE HUMAN BODY, MONTPELLIER, FRANCE -- RESORT AND RESIDENCES, HUALIEN, TAIWAN -- STETTIN 7 RESIDENCES, STOCKHOLM, SWEDEN -- KING STREET WEST, TORONTO, CANADA -- VTC TOWER, COPENHAGEN, DENMARK -- BIG IDEAS -- REFERENCES -- IMAGES -- 13. SIMULATING THE INVISIBLE: MAX FORDHAM DESIGNS LIGHT, AIR AND SOUND -- DESIGNING LIGHT AND AIR -- PARAMETRIC AND CLIMATE-LINKED DAYLIGHT MODELLING: THE HAYWARD GALLERY RENOVATION -- TOOLS FOR COMPLEXITY -- DESIGN FOR DAYLIGHT: MAXXI MUSEUM OF 21ST CENTURY ARTS, ROME, ITALY -- EXPERIENCE AND COMMUNICATING SOUND: BRITISH AIRWAYS LOUNGE FUTURES -- APPROACHES FOR SIMULATING DAYLIGHT: WESTMINSTER ABBEY -- SIMULATING TO UNDERSTAND AND CONVINCE -- REFERENCES -- IMAGES -- 14. WHITE ARCHITECTS: BUILD THE FUTURE -- KIRUNA 4-EVER -- WHITE'S SPECIALIST TEAMS -- STOCKHOLM'S SEB BANK HEADQUARTERS -- WHITE DESIGNING DAYLIGHT -- DESIGN TOOLS FOR THE FUTURE -- REFERENCES -- IMAGES -- 15. CORE: INTEGRATED COMPUTATION AND RESEARCH -- TOOL DEVELOPMENT AND COLLABORATIVE PLATFORMS -- MODELLING FRIT PATTERNS -- REMOTE SOLVING WORKFLOW -- DESIGN EXPLORER, HONEYBEE AND LADYBUG -- SENSING THE ENVIRONMENT -- PROJECT-DRIVEN RESEARCH -- REFERENCES -- IMAGES -- 16. SUPERSPACE: COMPUTING HUMAN-CENTRIC ARCHITECTURE -- COMPUTATIONAL METHODOLOGIES -- COMPUTATIONAL TOOLS -- TOP-DOWN OR BOTTOM-UP -- DATA-INTEGRATED DESIGN -- URBAN ECOLOGIES -- THE SPACE OF PEOPLE IN COMPUTATION -- VISUALISATION AND SPATIALISATION -- COMPUTING ENVIRONMENT AND GENERATING DESIGN -- REFERENCES -- IMAGES -- 17. ZHACODE: SKETCHING WITH PERFORMANCE -- UNIVERSITY COMPLEX-SOLAR SHADING -- STUDIES FOR WIND AND VISIBILITY ANALYSIS -- SAMBA COMPETITION -- SIMPLE-YET CUSTOM REAL-TIME SOLVERS -- REFERENCES -- IMAGES -- 18. WEWORK: BUILDING DATA FOR DESIGN FEEDBACK

Additional Edition: Print version Peters, Brady Computing the Environment Newark : John Wiley & Sons, Incorporated,c2018 ISBN 9781119097891

Language: English

Keywords: Electronic books ; Electronic books

URL: FULL  ((OIS Credentials Required))

URL: FULL  ((OIS Credentials Required))

UID:

Format: 348 S.

ISBN: 9783746635637

Series Statement: Friekes Buchladen, Band 2

Content: Eigentlich ist Frieke überglücklich mit Bengt - bis sie plötzlich merkt, dass an der Warnung: Einmal Junggeselle - immer Junggeselle, doch etwas dran sein könnte. Gut, dass es ihre beste Freundin Emma gibt. Als Zwillingsmama weiß sie immer für jedes Problem eine Lösung. Erst aber muss sie ihr eigenes Leben in den Griff bekommen, nachdem Ehemann Torben sie von heute auf morgen sitzen gelassen hat. Und so gerät Frieke emotional zusehends in Seenot. Außerdem ist ihr morgens immer so übel .

Language: German

UID:

Format: xii, 271 Seiten

ISBN: 978-0-226-45262-3 , 0-226-45262-X

Content: Includes bibliographical references and index

Language: English

UID:

Format: Seite 24-27

UID:

Format: 114 Min.

Series Statement: Die Theater-Edition

UID:

Format: 286 S.

Edition: 2. Aufl.

ISBN: 9783746636092

Content: Frieke zählt die Tage bis Heiligabend, sie kann gar nicht mehr aufhören, das alte Kapitänshaus oder die Buchhandlung zu schmücken. Nur warum liegt jeden Adventstag ein Briefchen vor ihrer Haustür? Was hat dieser besondere Adventskalender zu bedeuten? Und warum ist Bengt so abweisend, seit sie im Überschwang der Schwangerschaftshormone darüber gesprochen hat, dass sie ja heiraten könnten?

Language: German

UID:

Format: 320 S.

ISBN: 9783746633237

Series Statement: Band 2

Content: Die Toten am Salzhaff Die verdeckte Ermittlerin Emma Klar soll einen Mann beschatten, der wegen Totschlags zehn Jahre im Gefängnis saß, dessen Tatmotiv jedoch unklar geblieben ist. Christoph Klausen verhält sich zunächst völlig unauffällig, doch dann werden in einer Ferienanlage an der Ostsee zwei grausam zugerichtete Leichen gefunden. Emma glaubt, in Klausens Vergangenheit eine Verbindung zu den Toten zu erkennen. Sie heftet sich eigenmächtig an seine Fersen - und kommt ihm dabei gefährlich nahe ...

Language: German

UID:

Format: 1 online resource (415 pages)

Edition: 1st ed.

ISBN: 9780124173071

Note: Front Cover -- Sharing Data and Models in Software Engineering -- Copyright -- Why this book? -- Foreword -- Contents -- List of Figures -- Chapter 1: Introduction -- 1.1 Why Read This Book? -- 1.2 What Do We Mean by ``Sharing''? -- 1.2.1 Sharing Insights -- 1.2.2 Sharing Models -- 1.2.3 Sharing Data -- 1.2.4 Sharing Analysis Methods -- 1.2.5 Types of Sharing -- 1.2.6 Challenges with Sharing -- 1.2.7 How to Share -- 1.3 What? (Our Executive Summary) -- 1.3.1 An Overview -- 1.3.2 More Details -- 1.4 How to Read This Book -- 1.4.1 Data Analysis Patterns -- 1.5 But What About ...? (What Is Not in This Book) -- 1.5.1 What About ``Big Data''? -- 1.5.2 What About Related Work? -- 1.5.3 Why All the Defect Prediction and Effort Estimation? -- 1.6 Who? (About the Authors) -- 1.7 Who Else? (Acknowledgments) -- Part I: Data Mining for Managers -- Chapter 2: Rules for Managers -- 2.1 The Inductive Engineering Manifesto -- 2.2 More Rules -- Chapter 3: Rule #1: Talk to the Users -- 3.1 Users Biases -- 3.2 Data Mining Biases -- 3.3 Can We Avoid Bias? -- 3.4 Managing Biases -- 3.5 Summary -- Chapter 4: Rule #2: Know the Domain -- 4.1 Cautionary Tale #1: ``Discovering'' Random Noise -- 4.2 Cautionary Tale #2: Jumping at Shadows -- 4.3 Cautionary Tale #3: It Pays to Ask -- 4.4 Summary -- Chapter 5: Rule #3: Suspect Your Data -- 5.1 Controlling Data Collection -- 5.2 Problems with Controlled Data Collection -- 5.3 Rinse (and Prune) Before Use -- 5.3.1 Row Pruning -- 5.3.2 Column Pruning -- 5.4 On the Value of Pruning -- 5.5 Summary -- Chapter 6: Rule #4: Data Science Is Cyclic -- 6.1 The Knowledge Discovery Cycle -- 6.2 Evolving Cyclic Development -- 6.2.1 Scouting -- 6.2.2 Surveying -- 6.2.3 Building -- 6.2.4 Effort -- 6.3 Summary -- Part II: Data Mining: A Technical Tutorial -- Chapter 7: Data Mining and SE -- 7.1 Some Definitions -- 7.2 Some Application Areas , 12.6.3.3 Settings -- 12.6.3.4 Chunk (main function) -- 12.6.4 Support Utilities -- 12.6.4.1 Some standard tricks -- 12.6.4.2 Tree iterators -- 12.6.4.3 Pretty printing -- 12.7 Putting It all Together -- 12.7.1 _nasa93 -- 12.8 Using CHUNK -- 12.9 Closing Remarks -- Chapter 13: Cross-Company Learning: Handling the Data Drought -- 13.1 Motivation -- 13.2 Setting the Ground for Analyses -- 13.2.1 Wait ... Is This Really CC Data? -- 13.2.2 Mining the Data -- 13.2.3 Magic Trick: NN Relevancy Filtering -- 13.3 Analysis #1: Can CC Data be Useful for an Organization? -- 13.3.1 Design -- 13.3.2 Results from Analysis #1 -- 13.3.3 Checking the Analysis #1 Results -- 13.3.4 Discussion of Analysis #1 -- 13.4 Analysis #2: How to Cleanup CC Data for Local Tuning? -- 13.4.1 Design -- 13.4.2 Results -- 13.4.3 Discussions -- 13.5 Analysis #3: How Much Local Data Does an Organization Need for a Local Model? -- 13.5.1 Design -- 13.5.2 Results from Analysis #3 -- 13.5.3 Checking the Analysis #3 Results -- 13.5.4 Discussion of Analysis #3 -- 13.6 How Trustworthy Are These Results? -- 13.7 Are These Useful in Practice or Just Number Crunching? -- 13.8 What's New on Cross-Learning? -- 13.8.1 Discussion -- 13.9 What's the Takeaway? -- Chapter 14: Building Smarter Transfer Learners -- 14.1 What Is Actually the Problem? -- 14.2 What Do We Know So Far? -- 14.2.1 Transfer Learning -- 14.2.2 Transfer Learning and SE -- 14.2.3 Data Set Shift -- 14.3 An Example Technology: TEAK -- 14.4 The Details of the Experiments -- 14.4.1 Performance Comparison -- 14.4.2 Performance Measures -- 14.4.3 Retrieval Tendency -- 14.5 Results -- 14.5.1 Performance Comparison -- 14.5.2 Inspecting Selection Tendencies -- 14.6 Discussion -- 14.7 What Are the Takeaways? -- Chapter 15: Sharing Less Data (Is a Good Thing) -- 15.1 Can We Share Less Data? -- 15.2 Using Less Data -- 15.3 Why Share Less Data? , 15.3.1 Less Data Is More Reliable -- 15.3.2 Less Data Is Faster to Discuss -- 15.3.3 Less Data Is Easier to Process -- 15.4 How to Find Less Data -- 15.4.1 Input -- 15.4.2 Comparisons to Other Learners -- 15.4.3 Reporting the Results -- 15.4.4 Discussion of Results -- 15.5 What's Next? -- Chapter 16: How to Keep Your Data Private -- 16.1 Motivation -- 16.2 What Is PPDP and Why Is It Important? -- 16.3 What Is Considered a Breach of Privacy? -- 16.4 How to Avoid Privacy Breaches? -- 16.4.1 Generalization and Suppression -- 16.4.2 Anatomization and Permutation -- 16.4.3 Perturbation -- 16.4.4 Output Perturbation -- 16.5 How Are Privacy-Preserving Algorithms Evaluated? -- 16.5.1 Privacy Metrics -- 16.5.2 Modeling the Background Knowledge of an Attacker -- 16.6 Case Study: Privacy and Cross-Company Defect Prediction -- 16.6.1 Results and Contributions -- 16.6.2 Privacy and CCDP -- 16.6.3 CLIFF -- 16.6.4 MORPH -- 16.6.5 Example of CLIFF& -- MORPH -- 16.6.6 Evaluation Metrics -- 16.6.7 Evaluating Utility via Classification -- 16.6.8 Evaluating Privatization -- 16.6.8.1 Defining privacy -- 16.6.9 Experiments -- 16.6.9.1 Data -- 16.6.10 Design -- 16.6.11 Defect Predictors -- 16.6.12 Query Generator -- 16.6.13 Benchmark Privacy Algorithms -- 16.6.14 Experimental Evaluation -- 16.6.15 Discussion -- 16.6.16 Related Work: Privacy in SE -- 16.6.17 Summary -- Chapter 17: Compensating for Missing Data -- 17.1 Background Notes on SEE and Instance Selection -- 17.1.1 Software Effort Estimation -- 17.1.2 Instance Selection in SEE -- 17.2 Data Sets and Performance Measures -- 17.2.1 Data Sets -- 17.2.2 Error Measures -- 17.3 Experimental Conditions -- 17.3.1 The Algorithms Adopted -- 17.3.2 Proposed Method: POP1 -- 17.3.3 Experiments -- 17.4 Results -- 17.4.1 Results Without Instance Selection -- 17.4.2 Results with Instance Selection -- 17.5 Summary , 21.2 Related Work , Chapter 18: Active Learning: Learning More with Less -- 18.1 How Does the QUICK Algorithm Work? -- 18.1.1 Getting Rid of Similar Features: Synonym Pruning -- 18.1.2 Getting Rid of Dissimilar Instances: Outlier Pruning -- 18.2 Notes on Active Learning -- 18.3 The Application and Implementation Details of QUICK -- 18.3.1 Phase 1: Synonym Pruning -- 18.3.2 Phase 2: Outlier Removal and Estimation -- 18.3.3 Seeing QUICK in Action with a Toy Example -- 18.3.3.1 Phase 1: Synonym pruning -- 18.3.3.2 Phase 2: Outlier removal and estimation -- 18.4 How the Experiments Are Designed -- 18.5 Results -- 18.5.1 Performance -- 18.5.2 Reduction via Synonym and Outlier Pruning -- 18.5.3 Comparison of QUICK vs. CART -- 18.5.4 Detailed Look at the Statistical Analysis -- 18.5.5 Early Results on Defect Data Sets -- 18.6 Summary -- Part IV: Sharing Models -- Chapter 19: Sharing Models: Challenges and Methods -- Chapter 20: Ensembles of Learning Machines -- 20.1 When and Why Ensembles Work -- 20.1.1 Intuition -- 20.1.2 Theoretical Foundation -- 20.2 Bootstrap Aggregating (Bagging) -- 20.2.1 How Bagging Works -- 20.2.2 When and Why Bagging Works -- 20.2.3 Potential Advantages of Bagging for SEE -- 20.3 Regression Trees (RTs) for Bagging -- 20.4 Evaluation Framework -- 20.4.1 Choice of Data Sets and Preprocessing Techniques -- 20.4.1.1 PROMISE data -- 20.4.1.2 ISBSG data -- 20.4.2 Choice of Learning Machines -- 20.4.3 Choice of Evaluation Methods -- 20.4.4 Choice of Parameters -- 20.5 Evaluation of Bagging+RTs in SEE -- 20.5.1 Friedman Ranking -- 20.5.2 Approaches Most Often Ranked First or Second in Terms of MAE, MMRE and PRED(25) -- 20.5.3 Magnitude of Performance Against the Best -- 20.5.4 Discussion -- 20.6 Further Understanding of Bagging+RTs in SEE -- 20.7 Summary -- Chapter 21: How to Adapt Models in a Dynamic World -- 21.1 Cross-Company Data and Questions Tackled , Chapter 8: Defect Prediction -- 8.1 Defect Detection Economics -- 8.2 Static Code Defect Prediction -- 8.2.1 Easy to Use -- 8.2.2 Widely Used -- 8.2.3 Useful -- Chapter 9: Effort Estimation -- 9.1 The Estimation Problem -- 9.2 How to Make Estimates -- 9.2.1 Expert-Based Estimation -- 9.2.2 Model-Based Estimation -- 9.2.3 Hybrid Methods -- Chapter 10: Data Mining (Under the Hood) -- 10.1 Data Carving -- 10.2 About the Data -- 10.3 Cohen Pruning -- 10.4 Discretization -- 10.4.1 Other Discretization Methods -- 10.5 Column Pruning -- 10.6 Row Pruning -- 10.7 Cluster Pruning -- 10.7.1 Advantages of Prototypes -- 10.7.2 Advantages of Clustering -- 10.8 Contrast Pruning -- 10.9 Goal Pruning -- 10.10 Extensions for Continuous Classes -- 10.10.1 How RTs Work -- 10.10.2 Creating Splits for Categorical Input Features -- 10.10.3 Splits on Numeric Input Features -- 10.10.4 Termination Condition and Predictions -- 10.10.5 Potential Advantages of RTs for Software Effort Estimation -- 10.10.6 Predictions for Multiple Numeric Goals -- Part III: Sharing Data -- Chapter 11: Sharing Data: Challenges and Methods -- 11.1 Houston, We Have a Problem -- 11.2 Good News, Everyone -- Chapter 12: Learning Contexts -- 12.1 Background -- 12.2 Manual Methods for Contextualization -- 12.3 Automatic Methods -- 12.4 Other Motivation to Find Contexts -- 12.4.1 Variance Reduction -- 12.4.2 Anomaly Detection -- 12.4.3 Certification Envelopes -- 12.4.4 Incremental Learning -- 12.4.5 Compression -- 12.4.6 Optimization -- 12.5 How to Find Local Regions -- 12.5.1 License -- 12.5.2 Installing CHUNK -- 12.5.3 Testing Your Installation -- 12.5.4 Applying CHUNK to Other Models -- 12.6 Inside CHUNK -- 12.6.1 Roadmap to Functions -- 12.6.2 Distance Calculations -- 12.6.2.1 Normalize -- 12.6.2.2 SquaredDifference -- 12.6.3 Dividing the Data -- 12.6.3.1 FastDiv -- 12.6.3.2 TwoDistantPoints

Additional Edition: Print version Menzies, Tim Sharing Data and Models in Software Engineering San Diego : Elsevier Science & Technology,c2014 ISBN 9780124172951

Language: English

Keywords: Electronic books ; Electronic books

URL: FULL  ((OIS Credentials Required))

URL: FULL  ((OIS Credentials Required))