5 research outputs found
Learning to Predict Combinatorial Structures
The major challenge in designing a discriminative learning algorithm for
predicting structured data is to address the computational issues arising from
the exponential size of the output space. Existing algorithms make different
assumptions to ensure efficient, polynomial time estimation of model
parameters. For several combinatorial structures, including cycles, partially
ordered sets, permutations and other graph classes, these assumptions do not
hold. In this thesis, we address the problem of designing learning algorithms
for predicting combinatorial structures by introducing two new assumptions: (i)
The first assumption is that a particular counting problem can be solved
efficiently. The consequence is a generalisation of the classical ridge
regression for structured prediction. (ii) The second assumption is that a
particular sampling problem can be solved efficiently. The consequence is a new
technique for designing and analysing probabilistic structured prediction
models. These results can be applied to solve several complex learning problems
including but not limited to multi-label classification, multi-category
hierarchical classification, and label ranking.Comment: PhD thesis, Department of Computer Science, University of Bonn
(submitted, December 2009
A recommendation framework based on automated ranking for selecting negotiation agents. Application to a water market
This thesis presents an approach which relies on automatic learning and
data mining techniques in order to search the best group of items from a
set, according to the behaviour observed in previous groups.
The approach is applied to a framework of a water market system, which
aims to develop negotiation processes, where trading tables are built in
order to trade water rights from users. Our task will focus on predicting
which agents will show the most appropriate behaviour when are invited
to participate in a trading table, with the purpose of achieving the most
bene cial agreement.
This way, a model is developed and learns from past transactions occurred
in the market. Then, when a new trading table is opened in order to
trade a water right, the model predicts, taking into account the individual
features of the trading table, the behaviour of all the agents that can be
invited to join the negotiation, and thus, becoming potential buyers of the
water right.
Once the model has made the predictions for a trading table, the agents
are ranked according to their probability (which has been assigned by the
model) of becoming a buyer in that negotiation. Two di erent methods are
proposed in the thesis for dealing with the ranked participants. Depending
on the method used, from this ranking we can select the desired number of
participants for making the group, or choose only the top user of the list
and rebuild the model adding some aggregate information in order to throw
a more detailed prediction.Dura Garcia, EM. (2011). A recommendation framework based on automated ranking for selecting negotiation agents. Application to a water market. http://hdl.handle.net/10251/15875Archivo delegad