Hakutulokset: Hakutulos
yhteensä 22 hakutulosta
Pattern Recognition and Machine Learning
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, …
Cumulative Sum Charts and Charting for Quality Improvement
Cumulative sum (CUSUM) control charting is a valuable tool for detecting and diagnosing persistent shifts in series of readings. It is used in traditional statistical process …
Feedforward Neural Network Methodology
The decade prior to publication has seen an explosive growth in com- tational speed and memory and a rapid enrichment in our understa- ing of arti?cial neural networks. These two …
Estimation of Dependences Based on Empirical Data
Twenty-?ve years have passed since the publication of the Russian version of the book Estimation of Dependencies Based on Empirical Data (EDBED for short). Twen- ?ve years is a …
Nonlinear Dimensionality Reduction
Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and …
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, …
Computational Methods in Biometric Authentication
Biometrics, the science of using physical traits to identify individuals, is playing an increasing role in our security-conscious society and across the globe. Biometric …
Probabilistic Conditional Independence Structures
Probabilistic Conditional Independence Structures provides the mathematical description of probabilistic conditional independence structures; the author uses non-graphical methods …
Probabilistic Networks and Expert Systems
WINNER OF THE 2001 DEGROOT PRIZE! Probabilistic expert systems are graphical networks that support the modelling of uncertainty and decisions in large complex domains, while …
The Nature of Statistical Learning Theory
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of …
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, …
Bayesian Networks and Decision Graphs
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural …