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Recently, the ICT field has seen a shift from machine centered
focuses to human and user knowledge-based approaches. However, as
the priorities shift, questions arise on how to detect and monitor
users' behaviour. Human Behavior Recognition Technologies:
Intelligent Applications for Monitoring and Security addresses
these questions and more as it takes an insightful glance into the
applications and dependability of behaviour detection. In addition,
this comprehensive publication looks into the social, ethical, and
legal implications of these areas. Researchers and practitioners
interested in the computational aspects of behaviour monitoring as
well as the ethical and legal implications will find this reference
source beneficial.
A Proven, Hands-On Approach for Students without a Strong
Statistical Foundation Since the best-selling first edition was
published, there have been several prominent developments in the
field of machine learning, including the increasing work on the
statistical interpretations of machine learning algorithms.
Unfortunately, computer science students without a strong
statistical background often find it hard to get started in this
area. Remedying this deficiency, Machine Learning: An Algorithmic
Perspective, Second Edition helps students understand the
algorithms of machine learning. It puts them on a path toward
mastering the relevant mathematics and statistics as well as the
necessary programming and experimentation. New to the Second
Edition Two new chapters on deep belief networks and Gaussian
processes Reorganization of the chapters to make a more natural
flow of content Revision of the support vector machine material,
including a simple implementation for experiments New material on
random forests, the perceptron convergence theorem, accuracy
methods, and conjugate gradient optimization for the multi-layer
perceptron Additional discussions of the Kalman and particle
filters Improved code, including better use of naming conventions
in Python Suitable for both an introductory one-semester course and
more advanced courses, the text strongly encourages students to
practice with the code. Each chapter includes detailed examples
along with further reading and problems. All of the code used to
create the examples is available on the author's website.
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