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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.
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...
Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications
By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed into an algebraically simple form, which allows for computationally efficient algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians only, (2) Gaussian mixture filtering for strong nonlinearities, (3) Gaussian process filtering for purely data-driven scenarios. For each setting, efficient algorithms are derived and applied to real-world problems.
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.
Essential C# fast
A quick and practical introduction to the C# programming language. The text includes complete programing examples that highlight the core features of this language. In this book you will learn about: Using C# with a traditional compile run cycle, using C# within the Developer Studio environment, different data types supported in C#, control structures and input and output (i/o) in C#, key features of C# and their relationship to C, C++, Java and other programming languages.
Engineering Mathematics and Artificial Intelligence
The fields of Artificial Intelligence (AI) and Machine Learning (ML) have grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This book represents a key reference for anybody interested in the intersection between mathematics and AI/ML and provides an overview of the current research streams. Engineering Mathematics and Artificial Intelligence: Foundations, Methods, and Applications discusses the theory behind ML and shows how mathematics can be used in AI. The book illustrates how to improve existing algorithms by using advanced mathematics and offers cutting-edge AI technologies. The book goes on to discuss how ML can support mathematical modeling and how to simulate data by using artificial neural networks. Future integration between ML and complex mathematical techniques is also highlighted within the book. This book is written for researchers, practitioners, engineers, and AI consultants.
Machine Learning and Knowledge Discovery in Databases. Research Track
The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and f...
The AI-Savvy Leader
Leaders, don't let AI get the best of you. The AI transformation is underway, but where are the leaders who will ensure their companies implement AI successfully and responsibly? Up until now, leaders have largely ceded their role in the AI transformation, pushing strategy formulation out to tech teams and leaving investment decisions to groups that don't have a full view of the organization or its goals. Just when responsible leadership is more crucial than ever, leaders are abdicating their role in understanding and executing in the new world of human-machine collaboration. A generation of AI transformation failures awaits if leaders don't connect their use of AI to their strategies. This ...
Metric Learning
Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data....
