Events 2007

21.9.2007 HIIT Seminar: Janey Yu

HIIT seminars in fall 2007 will be held in hall **B222** of Exactum,
on Fridays starting at 10:15 a.m. Coffee available from 10.

Sep 21:
  Janey Yu
  Discriminative Training for Structured Predictions: An Efficient
  Optimization Method

We consider structured prediction problems with a parametrized linear
prediction function. Corresponding to such type of problems are many
applications: sentence alignment, image segmentation, and HMM, for
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14.9.2007 HIIT Seminar: Attila Egri-Nagy

HIIT seminars in fall 2007 will be held in hall **B222** of Exactum, on Fridays starting at 10:15 a.m. Coffee available from 10.

Fri Sep 14
Dr. Attila Egri-Nagy
Royal Society Wolfson BioComputation Research Lab, University of Hertfordshire, United Kingdom

Algebraic Hierarchical Decomposition of Finite State Automata and its Biological Applications

Abstract:
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12.9.2007 Special Guest Lecture by Phokion G. Kolaitis

 Special Guest Lecture by Phokion G. Kolaitis

(IBM Almaden Research Center, San Jose, CA)

      on Wednesday 12th September at 14:15
                 Exactum, room C222


Title:  Schema mappings and data exchange

Abstract:
Schema mappings are high-level specifications that describe the
relationship between  database schemas. Schema mappings are prominent
in several different areas of database management, including database
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12.9.2007: HIIT Board meeting 4/2007

HIIT Board meeting 4/2007 will be held on 12 September, 2007 at 11:00-13:30 at University of Helsinki, Department of Computer Science, Gustaf Hällströmin katu 2B, Helsinki. Meeting agenda
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7.9.2007 HIIT Seminar: Tomi Silander

HIIT seminars in fall 2007 will be held in hall **B222** of Exactum,
on Fridays starting at 10:15 a.m. Coffee available from 10.

Fri Sep 7
Tomi Silander
On Learning The Most Probable Bayesian Network

Abstract:
Learning the MAP Bayesian network for a complete discrete data is known
to be an NP-hard problem. Consequently, much of the research effort has
been concentrated on developing heuristics for this important task.
However, recent advances in exact methods for learning Bayesian Networks
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