The Mahalanobis-Taguchi data handling and pattern recognition system is widely established-- built and extended from the original quality control precepts of Genichi Taguchi. But the MT system is not always well understood. This new book makes the system much more vivid and concrete with real-life applications in a wide variety of disciplines from industry to general commerce. The book offers a clear computational method to show the user how to actually apply the system to real manufacturing control problems. With the renowned international industry background of the three authors and their historic ties to Genichi Taguchi, this book will bring a unique insight into how to get the most benefits from the MT System. The book offers an overview of pattern recognition issues and the precepts of the MT system. explains the merits of the MT System and its computational methods. shows how to handle data with the MT System and extract useful information. provides a useful comparison of the advantages and disadvantages between traditional Artificial Intelligence systems and the MT system. provides case study examples of MT Systems applications.

This book presents how to apply recent machine learning (deep learning) methods for the task of speech quality prediction. The author shows how recent advancements in machine learning can be leveraged for the task of speech quality prediction and provides an in-depth analysis of the suitability of different deep learning architectures for this task. The author then shows how the resulting model outperforms traditional speech quality models and provides additional information about the cause of a quality impairment through the prediction of the speech quality dimensions of noisiness, coloration, discontinuity, and loudness.

The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. - Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning - Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering - Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection

The LNCS volume 12878 constitutes the proceedings of the 15th Chinese Conference on Biometric Recognition, held in Shanghai, China, in September 2021. The 53 papers presented in this book were carefully reviewed and selected from 72 submissions. The papers cover a wide range of topics such as multi-modal biometrics and emerging biometrics; hand biometrics; facial biometrics; and speech biometrics.

This two-volume set LNCS 11662 and 11663 constitutes the refereed proceedings of the 16th International Conference on Image Analysis and Recognition, ICIAR 2019, held in Waterloo, ON, Canada, in August 2019. The 58 full papers presented together with 24 short and 2 poster papers were carefully reviewed and selected from 142 submissions. The papers are organized in the following topical sections: Image Processing; Image Analysis; Signal Processing Techniques for Ultrasound Tissue Characterization and Imaging in Complex Biological Media; Advances in Deep Learning; Deep Learning on the Edge; Recognition; Applications; Medical Imaging and Analysis Using Deep Learning and Machine Intelligence; Image Analysis and Recognition for Automotive Industry; Adaptive Methods for Ultrasound Beamforming and Motion Estimation.

The two-volume set LNCS 7324/7325 constitutes the refereed proceedings of the 9th International Conference on Image and Recognition, ICIAR 2012, held in Aveiro, Portugal, in June 2012. The 107 revised full papers presented were carefully reviewed and selected from 207 submissions. The papers are organized in topical sections on clustering and classification; image processing; image analysis; motion analysis and tracking; shape representation; 3D imaging; applications; biometrics and face recognition; human activity recognition; biomedical image analysis; retinal image analysis; and call detection and modeling.

This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included. Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on. EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines. Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest. We believe that many effective ways to exploit group differences' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.

This book discusses the simulation of conversations through a novel approach of predicting speech quality based on the interactions of two simulated interlocutors. The author describes the setup of a simulation environment that is capable of simulating human dialogue on the speech level. The impact of delay and bursty packet loss on VoIP conversations is investigated and modeled for the use in the simulation. Based on parameters extracted from simulated conversations, the author proposes extensions to the E-model, a parametric model standardized by the International Telecommunications Union, in order to predict the quality of the simulated conversations. The author shows that predictions based on the simulated conversations outperform models that rely on the transmission parameters alone.

Quality of Telephone-Based Spoken Dialogue Systems is a systematic overview of assessment, evaluation, and prediction methods for the quality of services such as travel and touristic information, phone-directory and messaging, or telephone-banking services. A new taxonomy of quality-of-service is presented which serves as a tool for classifying assessment and evaluation methods, for planning and interpreting evaluation experiments, and for estimating quality. A broad overview of parameters and evaluation methods is given, both on a system-component level and for a fully integrated system. Three experimental investigations illustrate the relationships between system characteristics and perceived quality. The resulting information is needed in all phases of system specification, design, implementation, and operation. Although Quality of Telephone-Based Spoken Dialogue Systems is written from the perspective of an engineer in telecommunications, it is an invaluable source of information for professionals in signal processing, communication acoustics, computational linguistics, speech and language sciences, human factor design and ergonomics