Før Memmo var notatene mine spredt overalt i PDF-er. Nå samler et arbeidsområde alt på ett sted – jeg ser akkurat hva som gjenstår å studere.
This book is the first of its kind to discuss error estimation with a model-based approach. From the basics of classifiers and error estimators to distributional and Bayesian theory, it covers important topics and essential issues pertaining to the scientific validity of pattern classification.
Error Estimation for Pattern Recognition focuses on error estimation, which is a broad and poorly understood topic that reaches all research areas using pattern classification. It includes model-based approaches and discussions of newer error estimators such as bolstered and Bayesian estimators. This book was motivated by the application of pattern recognition to high-throughput data with limited replicates, which is a basic problem now appearing in many areas. The first two chapters cover basic issues in classification error estimation, such as definitions, test-set error estimation, and training-set error estimation. The remaining chapters in this book cover results on the performance and representation of training-set error estimators for various pattern classifiers.
Additional features of the book include:
• The latest results on the accuracy of error estimation
• Performance analysis of re-substitution, cross-validation, and bootstrap error estimators using analytical and simulation approaches
• Highly interactive computer-based exercises and end-of-chapter problems
This is the first book exclusively about error estimation for pattern recognition.
Ulisses M. Braga Neto is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, USA. He received his PhD in Electrical and Computer Engineering from The Johns Hopkins University. Dr. Braga Neto received an NSF CAREER Award for his work on error estimation for pattern recognition with applications in genomic signal processing. He is an IEEE Senior Member.
Edward R. Dougherty is a Distinguished Professor, Robert F. Kennedy ’26 Chair, and Scientific Director at the Center for Bioinformatics and Genomic Systems Engineering at Texas A&M University, USA. He is a fellow of both the IEEE and SPIE, and he has received the SPIE Presidents Award. Dr. Dougherty has authored several books including Epistemology of the Cell: A Systems Perspective on Biological Knowledge and Random Processes for Image and Signal Processing (Wiley-IEEE Press).
Før Memmo var notatene mine spredt overalt i PDF-er. Nå samler et arbeidsområde alt på ett sted – jeg ser akkurat hva som gjenstår å studere.
Memmos sammendrag er gull før eksamen. Jeg slipper å lese 800 sider to uker før – bare det viktigste.
AI-chatten har reddet meg kvelden før eksamen mer enn én gang. Jeg bare spør til jeg forstår – slipper å vente på svar i en studiegruppe.
Quizene treffer akkurat det jeg trenger å vite. Memmo holder styr på hva jeg sliter med – så jeg øver bare på det som er verdt det.
Flashcards med repetisjon over tid er magi. Memmo vet når jeg er i ferd med å glemme noe og viser det igjen.
AI-podkastene er min favoritt. Jeg lytter på vei til skolen og får en repetisjon uten å sitte foran en datamaskin.
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