Semantic analysis of multimedia content is an on going research
area that has gained a lot of attention over the last few years.
Additionally, Machine Learning techniques are widely used for multimedia
analysis with great success. This work presents a combined approach
to semantic adaptation of neural network classifiers in multimedia framework.
It is based on a fuzzy reasoning engine which is able to evaluate
the outputs and the confidence levels of the neural network classifier, using
a knowledge base. Improved image segmentation results are obtained,
which are used for adaptation of the network classifier, further increasing
its ability to provide accurate classification of the specific content.
pdf格式的英文文獻，是關于認知無線電網絡的，編者是加拿大桂爾夫大學的Qusay H. Mahmoud。ISBN：978-0-470-06196-1
章節內容：
1 Biologically Inspired Networking
2 The Role of Autonomic Networking in Cognitive Networks
3 Adaptive Networks
4 Self-Managing Networks
5 Machine Learning for Cognitive Networks: Technology Assessment
and Research Challenges
6 Cross-Layer Design and Optimization in Wireless Networks
等，共計13章，全書348頁，pdf文件383頁。
Text mining tries to solve the crisis of information overload by combining techniques from data mining, Machine Learning, natural language processing, information retrieval, and knowledge management. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, this book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches. Finally, it explores current real-world, mission-critical applications of text mining and link detection in such varied fields as M&A business intelligence, genomics research and counter-terrorism activities.
Pattern Analysis is the process of fi nding general relations in a set of data,
and forms the core of many disciplines, from neural networks to so-called syn-
tactical pattern recognition, from statistical pattern recognition to machine
learning and data mining. Applications of pattern analysis range from bioin-
formatics to document retrieval.
Evolutionary Computation (EC) deals with problem solving, optimization, and
machine learning techniques inspired by principles of natural evolution and ge-
netics. Just from this basic definition, it is clear that one of the main features of
the research community involved in the study of its theory and in its applications
is multidisciplinarity. For this reason, EC has been able to draw the attention of
an ever-increasing number of researchers and practitioners in several fields.
This introduction takes a visionary look at ideal cognitive radios (CRs) that inte-
grate advanced software-defined radios (SDR) with CR techniques to arrive at
radios that learn to help their user using computer vision, high-performance
speech understanding, global positioning system (GPS) navigation, sophisticated
adaptive networking, adaptive physical layer radio waveforms, and a wide range
of Machine Learning processes.
Although state of the art in many typical Machine Learning tasks, deep learning
algorithmsareverycostly interms ofenergyconsumption,duetotheirlargeamount
of required computations and huge model sizes. Because of this, deep learning
applications on battery-constrained wearables have only been possible through
wireless connections with a resourceful cloud. This setup has several drawbacks.
First, there are privacy concerns. Cloud computing requires users to share their raw
data—images, video, locations, speech—with a remote system. Most users are not
willing to do this. Second, the cloud-setup requires users to be connected all the
time, which is unfeasible given current cellular coverage. Furthermore, real-time
applications require low latency connections, which cannot be guaranteed using
the current communication infrastructure. Finally, wireless connections are very
inefficient—requiringtoo much energyper transferredbit for real-time data transfer
on energy-constrained platforms.
The present era of research and development is all about interdisciplinary studies
attempting to better comprehend and model our understanding of this vast universe.
The fields of biology and computer science are no exception. This book discusses
some of the innumerable ways in which computational methods can be used to
facilitate research in biology and medicine—from storing enormous amounts of
biological data to solving complex biological problems and enhancing the treatment
of various diseases.
