Efficient Learning Machines_ Theories, Concepts, and Applications for Engineers and System Designers [Awad & Khanna 2015-04-30].pdf

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About the Authors�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½xv
About the Technical Reviewers �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½xvii
Acknowledgments �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½xix
Chapter 1: Machine Learning �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½
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Chapter 2: Machine Learning and Knowledge Discovery �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½
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Chapter 3: Support Vector Machines for Classification�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½
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Chapter 4: Support Vector Regression �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½
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Chapter 5: Hidden Markov Model �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½
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Chapter 6: Bioinspired Computing: Swarm Intelligence�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½
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Chapter 7: Deep Neural Networks �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½
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Chapter 8: Cortical Algorithms �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½
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Chapter 9: Deep Learning �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½
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Chapter 10: Multiobjective Optimization �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½
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Chapter 11: Machine Learning in Action: Examples �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½
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Index �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½
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Chapter 1
Machine Learning
Nature is a self-made machine, more perfectly automated than any automated machine.
To create something in the image of nature is to create a machine, and it was by learning
the inner working of nature that man became a builder of machines.
—Eric Hoffer,
Reflections on the Human Condition
Machine learning
(ML) is a branch of artificial intelligence that systematically applies algorithms to
synthesize the underlying relationships among data and information. For example, ML systems can be
trained on automatic speech recognition systems (such as iPhone’s Siri) to convert acoustic information in a
sequence of speech data into semantic structure expressed in the form of a string of words.
ML is already finding widespread uses in web search, ad placement, credit scoring, stock market
prediction, gene sequence analysis, behavior analysis, smart coupons, drug development, weather
forecasting, big data analytics, and many more applications. ML will play a decisive role in the development
of a host of user-centric innovations.
ML owes its burgeoning adoption to its ability to characterize underlying relationships within large
arrays of data in ways that solve problems in big data analytics, behavioral pattern recognition, and
information evolution. ML systems can moreover be trained to categorize the changing conditions of a
process so as to model variations in operating behavior. As bodies of knowledge evolve under the influence
of new ideas and technologies, ML systems can identify disruptions to the existing models and redesign and
retrain themselves to adapt to and coevolve with the new knowledge.
The computational characteristic of ML is to generalize the
training experience
(or examples) and
output a hypothesis that estimates the target function. The generalization attribute of ML allows the system
to perform well on unseen data instances by accurately predicting the future data. Unlike other optimization
problems, ML does not have a well-defined function that can be optimized. Instead, training errors serve
as a catalyst to test learning errors. The process of generalization requires classifiers that input discrete or
continuous feature vectors and output a class.
The goal of ML is to predict future events or scenarios that are unknown to the computer. In 1959,
Arthur Samuel described ML as the “field of study that gives computers the ability to learn without
being explicitly programmed” (Samuel 1959). He concluded that programming computers to learn from
experience should eventually eliminate the need for much of this detailed programming effort. According
to Tom M. Mitchell’s definition of ML: “A computer program is said to learn from experience
E
with respect
to some class of tasks
T
and performance measure
P,
if its performance at tasks in
T,
as measured by
P,
improves with experience
E.”
Alan Turing’s seminal paper (Turing 1950) introduced a benchmark standard
for demonstrating machine intelligence, such that a machine has to be intelligent and responsive in a
manner that cannot be differentiated from that of a human being.
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Chapter 1
MaChine Learning
The learning process plays a crucial role in generalizing the problem by acting on its historical experience.
Experience exists in the form of training datasets, which aid in achieving accurate results on new and unseen
tasks. The training datasets encompass an existing problem domain that the learner uses to build a general
model about that domain. This enables the model to generate largely accurate predictions in new cases.
Key Terminology
To facilitate the reader’s understanding of the concept of ML, this section defines and discusses some key
multidisciplinary conceptual terms in relation to ML.
classifier.
A method that receives a new input as an unlabeled instance of an
observation or feature and identifies a category or class to which it belongs. Many
commonly used classifiers employ statistical inference (probability measure) to
categorize the best label for a given instance.
confusion matrix
(aka
error matrix).
A matrix that visualizes the performance of
the classification algorithm using the data in the matrix. It compares the predicted
classification against the actual classification in the form of false positive, true
positive, false negative and true negative information. A confusion matrix for
a two-class classifier system (Kohavi and Provost, 1998) follows:
accuracy
(aka
error rate).
The rate of correct (or incorrect) predictions made by the
model over a dataset. Accuracy is usually estimated by using an independent test set
that was not used at any time during the learning process. More complex accuracy
estimation techniques, such as
cross-validation
and
bootstrapping,
are commonly
used, especially with datasets containing a small number of instances.
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