Advances in Computers: Quality Software DevelopmentMarvin Zelkowitz Elsevier, 2006. gada 25. apr. - 344 lappuses This volume of Advances in Computers is number 66 in the series that began back in 1960. This series presents the ever changing landscape in the continuing evolution of the development of the computer and the field of information processing. Each year three volumes are produced presenting approximately 20 chapters that describe the latest technology in the use of computers today. Volume 66, subtitled "Quality software development," is concerned about the current need to create quality software. It describes the current emphasis in techniques for creating such software and in methods to demonstrate that the software indeed meets the expectations of the designers and purchasers of that software.
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No grāmatas satura
1.–5. rezultāts no 28.
v. lappuse
... . . . . . . . . . . . . . . . . . 44 2. Noise-Handling Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3. Ensemble-Partitioning Filter . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4. Modeling Methodology ...
... . . . . . . . . . . . . . . . . . 44 2. Noise-Handling Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3. Ensemble-Partitioning Filter . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4. Modeling Methodology ...
44. lappuse
... Noise-Handling Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.1. Class-Noise Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.2. Data Noise and Exceptions ...
... Noise-Handling Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.1. Class-Noise Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.2. Data Noise and Exceptions ...
45. lappuse
... noise can be classified into two types: attribute noise and class noise [10,17]. Attribute noise represents errors introduced in the attribute values of the instances (i.e., independent variables or features). Class noise are ...
... noise can be classified into two types: attribute noise and class noise [10,17]. Attribute noise represents errors introduced in the attribute values of the instances (i.e., independent variables or features). Class noise are ...
46. lappuse
... noise requires automatic noise-handling algorithms. This work focuses on class-noise filters to automatically detect and remove training instances suspected of being mislabeled. Quinlan [19] showed that when the level of noise increases ...
... noise requires automatic noise-handling algorithms. This work focuses on class-noise filters to automatically detect and remove training instances suspected of being mislabeled. Quinlan [19] showed that when the level of noise increases ...
47. lappuse
... noise rate nor the noise distribution in the dataset. Because there is no direct way to know which instances are noisy (as opposed to injecting artificial noise), we developed a technique called the efficiency paired comparison that ...
... noise rate nor the noise distribution in the dataset. Because there is no direct way to know which instances are noisy (as opposed to injecting artificial noise), we developed a technique called the efficiency paired comparison that ...
Saturs
43 | |
Requirements Management for Dependable Software Systems | 79 |
Mechanics of Managing Software Risk | 143 |
The PERFECT Approach to ExperienceBased Process Evolution | 173 |
The Opportunities Challenges and Risks of High Performance Computing in Computational Science and Engineering | 239 |
Author Index | 303 |
Subject Index | 311 |
Contents of Volumes in This Series | 323 |
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Advances in Computers: Quality Software Development Marvin Zelkowitz, Ph.D., MS, BS. Priekšskatījums nav pieejams - 2006 |
Bieži izmantoti vārdi un frāzes
activities algorithms analysis application artifacts B/CR behavior capability Capability Maturity Model Challenge classifiers CMMI code development code project code team company’s processes complex components computational science constraints cost cross-validation Data Mining dataset defects defined dependability development process documentation effects efficient Engrg Ensemble Filter Ensemble-Partitioning Filter estimate evolution efforts example experience FALCON project filtering level goals identify IEEE Software impact implementation improvement game plan Inspections interfaces investment iterations Machine Learning Maturity Model measured ment metrics noise noisy operational PEDAL framework predictions problems process architecture process change agents process evolution exercises process performance process set processors product planning project management PSPsm schedule SLOC software development software development processes Software Engineering Software Engineering Institute Software Metrics software process improvement software project software quality specific SPI method stakeholders SW-CMM Table tion TSPsm users validation verified WebGuide
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