| Optimization for Machine Learning |
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Google Tech Talks
March, 25 2008
ABSTRACT
S.V.N. Vishwanathan - Research Scientist
Regularized risk minimization is at the heart
of many machine learning algorithms. The
underlying objective function to be minimized
is convex, and often non-smooth. Classical
optimization algorithms cannot handle this
efficiently. In this talk we present two
algorithms for dealing with convex non-smooth
objective functions. First, we extend the
well known BFGS quasi-Newton algorithm to
handle non-smooth
functions. Second, we show how bundle methods
can be applied in a machine learning context.
We present both theoretical and experimental
justification of our algorithms.
Speaker: S.V.N. Vishwanathan - Research
Scientist - Zurich
S.V.N Vishwanathan is a principal researcher
in the Statistical Machine Learning program,
National ICT Australia with an adjunct
appointment at the College of Engineering and
Computer Science(CECS), Australian National
University. I got my Ph.D in 2002 from the
Department of Computer Science and Automation
(CSA) at the Indian Institute of Science.
Tags : google techtalks techtalk engedu talk talks googletechtalks education |
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Affichage : 3327
Durée : 3344 s |
| Search Engine Optimization SEO Tutorial -- WebBizIdeas |
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Search Engine Optimization SEO Tutorial by
WebBizIdeas is for beginners. We will cover
SEO techniques that you can use TODAY that
will increase your search engine rankings.
We will go over the definition of search
engine optimization, organic results, PPC,
keyword research, competition research,
competition analysis, on page & off page
optimization, Meta tags, header tags, keyword
density, URLs, site maps, xml site maps,
google webmaster tools, link development,
directory submission, local directories,
online yellow pages, one-way links, two-way
links, three-way links, article submission,
rss feed distribution, blog submission, and
online press release optimization.
Tags : Search Engine Optimization SEO Tutotial WebBizIdeas internet marketing increase search engine rankings techniques |
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Affichage : 2421
Durée : 543 s |
| Lecture 1 | Convex Optimization I (Stanford) |
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Professor Stephen Boyd, of the Stanford
University Electrical Engineering department,
gives the introductory lecture for the
course, Convex Optimization I (EE 364A).
Convex Optimization I concentrates on
recognizing and solving convex optimization
problems that arise in engineering. Convex
sets, functions, and optimization problems.
Basics of convex analysis. Least-squares,
linear and quadratic programs, semidefinite
programming, minimax, extremal volume, and
other problems. Optimality conditions,
duality theory, theorems of alternative, and
applications. Interior-point methods.
Applications to signal processing, control,
digital and analog circuit design,
computational geometry, statistics, and
mechanical engineering.
Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=3940D
D956CDF0622
EE 364A Course Website:
http://www.stanford.edu/class/ee364
Stanford University:
http://www.stanford.edu/
Stanford University Channel on YouTube:
http://www.youtube.com/stanford/
Tags : science electrical engineering technology convex optimization least squares constraint function portfolio circuit data fitting ellipsoid control signal processing |
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Affichage : 3115
Durée : 4833 s |
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