Tuesday, September 18
16:10-18:30 Plenary session Auditorium
ECML Poster Highlights
Session
Chairs: Stan Matwin and Dunja
Mladenic
Stepwise
Induction of Multi-Target Model Trees
by Annalisa Appice, Sao
Deroski
Comparing
Rule Measures for Predictive Associaton Rules
by Paulo J. Azevedo, Alípio
M. Jorge
User
Oriented Hierarchical Information Organization and Retrieval
by Korinna Bade, Marcel Hermkes, Andreas Nürnberger
Learning a
Classifier with Very Few Examples: Analogy Based and Knowledge Based Generation
of New Examples for Character Recognition
by Sabri Bayoudh, Harold Mouchère,
Laurent Miclet, Eric Anquetil
Weighted
Kernel Regression for Predicting Changing Dependencies
by Steven Busuttil, Yuri Kalnishkan
Counter-Example
Generation-Based One-Class Classification
by András Bánhalmi,
András Kocsor, Róbert Busa-Fekete
Test-Cost
Sensitive Classification based on Conditioned Loss Functions
by Mumin Cebe,
Cigdem Gunduz-Demir
Probabilistic
Models for Action-based Chinese Dependency Parsing
by Xiangyu Duan,
Jun Zhao, Bo Xu
Learning
Directed Probabilistic Logical Models: Ordering-search versus Structure-search
by Daan Fierens,
Jan Ramon, Maurice Bruynooghe, Hendrik
Blockeel
A Simple
Lexicographic Ranker and Probability Estimator
by Peter Flach, Edson
Takashi Matsubara
On
Minimizing the Position Error in Label Ranking
by Eyke Hüllermeier,
Johannes Fürnkranz
On Phase
Transitions in Learning Sparse Networks
by Goele Hollanders, Geert
Jan Bex, Marc Gyssens,
Ronald L. Westra,
Karl Tuyls
Semi-supervised
Collaborative Text Classification
by Rong Jin, Ming Wu, Rahul Sukthankar
Learning
from Relevant Tasks Only
by Samuel Kaski, Jaakko
Peltonen
An
Unsupervised Learning Algorithm for Rank Aggregation
by Alexandre Klementiev, Dan Roth,
Kevin Small
Ensembles
of multi-objective decision trees
by Dragi Kocev,
Celine Vens, Jan Struyf, Sao Deroski
Kernel-Based
Grouping of Histogram Data
by Tilman Lange, Joachim M. Buhmann
Active
Class Selection
by Rachel Lomasky, Carla E. Brodley, Matthew Aernecke, David Walt,
Mark Friedl
Sequence
labeling with Reinforcement Learning and Ranking Algorithms
by Francis Maes,
Ludovic Denoyer, Patrick Gallinari
Efficient Pairwise Classification
by
Scale-space
based Weak Regressors for Boosting
by
K-means
with Large and Noisy Constraint Sets
by Dan Pelleg, Dorit
Baras
Towards
Interactive Active Learning in Multi-View Feature Sets for Information
Extraction
by Katharina Probst, Rayid
Ghani
Principal
Component Analysis for Large Scale Problems with Lots of Missing Values
by Tapani Raiko,
Alexander Ilin, Juha Karhunen
Transfer
Learning in Reinforcement Learning Problems Through
Partial Policy Recycling
by Jan Ramon, Kurt Driessens, Tom Croonenborghs
Class
Noise Mitigation through Instance Weighting
by Umaa Rebbapragada,
Carla E. Brodley
Optimizing
Feature Sets for Structured Data
by Ulrich Rückert, Stefan Kramer
Roulette
Sampling for Cost-Sensitive Learning
by Victor S. Sheng, Charles X. Ling
Modeling
Highway Traffic Volumes
by Tomá ingliar,
Milo Hauskrecht
Undercomplete Blind Subspace Deconvolution via Linear Prediction
by Zoltán Szabó,
Barnabás Póczos, András Lőrincz
Learning
an Outlier-Robust Kalman Filter
by Jo-Anne Ting, Evangelos Theodorou, Stefan Schaal
Imitation
Learning Using Graphical Models
by Deepak Verma, Rajesh Rao
Nondeterministic
Discretization of Weights Improves Accuracy of Neural
Networks
by Marcin Wojnarski
Semi-Definite
Manifold Alignment
by Liang Xiong, Fei
Wang, Changshui Zhang
General
Solution for Supervised Graph Embedding
by Qubo You,
Multi-objective
Genetic Programming for Multiple Instance Learning
by Amelia Zafra, Sebastián
Ventura
Exploiting
Predicate, Term, and Feature Taxonomies in Propositionalization
by Monika áková, Filip
elezný
Multi-Party, Privacy-Preserving Distributed Data
Mining using a Game Theoretic Framework
by Hillol Kargupta,
Kamalika Das, Kun Liu
19:00-22:00 ECML
Poster Reception
at the