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Dataset Shift in Machine Learning
An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions. Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail...
Machine Learning Challenges
This book constitutes the refereed post-proceedings of the First PASCAL Machine Learning Challenges Workshop, MLCW 2005. 25 papers address three challenges: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; second, recognizing objects from a number of visual object classes in realistic scenes; third, recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.
Encoding Bioethics
Encoding Bioethics addresses important ethical concerns from the perspective of each of the stakeholders who will develop, deploy, and use artificial intelligence systems to support clinical decisions. Utilizing an applied ethical model of patient-centered care, this book considers the viewpoints of programmers, health system and health insurance leaders, clinicians, and patients when AI is used in clinical decision-making. The authors build on their respective experiences as a surgeon-bioethicist and a surgeon-AI developer to give the reader an accessible account of the relevant ethical considerations raised when AI systems are introduced into the physician-patient relationship.
Switching and Learning in Feedback Systems
This book presents the outcome of the European Summer School on Multi-agent Control, held in Maynooth, Ireland in September 2003. The past decade witnessed remarkable progress in the area of dynamic systems with the emergence of a number of powerful methods for both modeling and controlling uncertain dynamic systems. The first two parts of this book present tutorial lectures by leading researchers in the area introducing the reader to recent achievements on switching and control and on Gaussian processes. The third part is devoted to the presentation of original research contributions in the area; among the topics addressed are car control, bounding algorithms, networked control systems, the theory of linear systems, Bayesian modeling, and surveying multiagent systems.
Large-scale Kernel Machines
Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets. Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to outperform algorithms that simply process a random subset of the data. This volume offers researchers and engineers practical solutions for learning from large scale ...
Uncertainty in Mechanical Engineering III
Selected, peer reviewed papers from the 3rd International Conference on Uncertainty in Mechanical Engineering (ICUME 2018), November 15 - 16, 2018, Darmstadt, Germany
Automated Machine Learning and Meta-Learning for Multimedia
This book disseminates and promotes the recent research progress and frontier development on AutoML and meta-learning as well as their applications on computer vision, natural language processing, multimedia and data mining related fields. These are exciting and fast-growing research directions in the general field of machine learning. The authors advocate novel, high-quality research findings, and innovative solutions to the challenging problems in AutoML and meta-learning. This topic is at the core of the scope of artificial intelligence, and is attractive to audience from both academia and industry. This book is highly accessible to the whole machine learning community, including: researchers, students and practitioners who are interested in AutoML, meta-learning, and their applications in multimedia, computer vision, natural language processing and data mining related tasks. The book is self-contained and designed for introductory and intermediate audiences. No special prerequisite knowledge is required to read this book.
Inductive Logic
In designing the Handbook of the History of Logic, the Editors have taken the view that the history of logic holds more than an antiquarian interest, and that a knowledge of logic's rich and sophisticated development is, in various respects, relevant to the research programmes of the present day. Ancient logic is no exception. The present volume attests to the distant origins of some of modern logic's most important features, such as can be found in the claim by the authors of the chapter on Aristotle's early logic that, from its infancy, the theory of the syllogism is an example of an intuitionistic, non-monotonic, relevantly paraconsistent logic. Similarly, in addition to its comparative earliness, what is striking about the best of the Megarian and Stoic traditions is their sophistication and originality.
Pattern Recognition
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