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February 17, 2006 at 10:30-11:30 AM
Machine learning is to fit an accurate inductive model using labelled
training data to match a true model. One practical example is to figure
out if someone will default a mortgage loan. The major difficulty is that
the true model is never known for many real world problems, and any
assumptions can be wrong in general. The main stream research of machine
learning in the past 20 years has been focusing on rather sophisticated and
well-thought approaches to solve problems in classification, regression and
probability estimations. Very well known algorithms belonging to the family
of sophisticated approaches include Boosting, Bagging, SVM among others.
In this talk, we will introduce Randomized Decision Trees or RDT that can
be used effectively for classification, regression and probability
estimation problems. The training procedure of RDT incorporates some
surprisingly unconventional random factors. However, its accuracy in all
three major problems, i.e., classification, regression and probabilitly
estimation, are higher and significantly higher those very well known
approaches, i.e. Boosting, Bagging, SVM, MLR, and regression trees.
This talk offers the following insights:
1. Introduction of Randomized Decision Trees and its application in
classification, regression, probability estimation. Several actual
applications within IBM such as equity trading fraud, default payment,
information retrieval, storage compoment latency modelling will be included.
2. A fresh and unconventional look at effective machine learning and data
mining without strong assumptions.
Friday, February 10th, 2006 at 11:00 am at MEC201
One of the most prevalent phenomena of the 21st century is the
frequency in which multi national enterprises (MNE) are looking for
new location of the next link on the value chain, be it a
manufacturing facility or regional R&D headquarters, and the
continuous effort by governments, at all levels, to provide the
most attractive environment for such endeavours. The presentation
deals with two related issues. First, a general framework for multi
national enterprises location decision is offered, and the primary
factors considered by MNEs when setting up activities through
foreign direct investments are presented. This framework utilizes
positioning mapping techniques to map manager perceptions of
various countries in selected regions and to help policymakers make
their countries more appealing for such investments. Second, an
inquiry into the relationship between the quality of tertiary
educational personnel employed in manufacturing industries and in
universities and country competitiveness is discussed. The inquiry
uses data derived from the European Union. For both issues, future
research and generalizations in scope, geography and industrial
sectors are offered.
Friday, February 3rd, 2006 at 11:00 am at MEC201
The increasing demands for mobile communications in our
society have inspired the academic community to study access control
mechanisms in the presence of mobility. In Role-Based Access Control, a
given user is assigned a collection of roles (e.g. employee, faculty,
student, etc.). In turn, each role is assigned a collection of access
privileges. A user gains access to a resource by activating a role which
has the necessary privileges. Mobility adds a new dimension to RBAC,
since the services available to a given user also depend on the location
of the user, agreements between parties, and the technology underlying
the connection.
Consider the following example. The University of Wizbrau is equipped
with intelligent buildings, and students carry their wireless-enabled
laptops with them to class. While in the classroom, students have only
limited Internet access and they are not allowed to use e-mail, instant
messenger, or visit general web-sites. However, these activities are
allowed when done from the student lounge instead. Since the instructor
of the course needs a greater access to resources than the students,
those activities temporarily disabled to the students are available to
the instructor. For example, during a lecture, the instructor may
consult her e-mail to address a question raised by a student in an
e-mail message.
Traditional RBAC mechanisms associate privileges with roles ignoring
locations while our work incorporates location awareness enabling
scenarios like the one in our example.
This is joint work with Elsa Gunter (UIUC).