Explainable and interpretable machine learning and data mining.
|
DMKD |
2024 |
0 |
Efficient learning of large sets of locally optimal classification rules.
|
MLJ |
2023 |
1 |
Tree-based dynamic classifier chains.
|
MLJ |
2023 |
0 |
A flexible class of dependence-aware multi-label loss functions.
|
MLJ |
2022 |
0 |
Gradient-Based Label Binning in Multi-label Classification.
|
ECML/PKDD |
2021 |
2 |
A review of possible effects of cognitive biases on interpretation of rule-based machine learning models.
|
Artificial Intelligence |
2021 |
0 |
Learning Gradient Boosted Multi-label Classification Rules.
|
ECML/PKDD |
2020 |
19 |
Permutation Learning via Lehmer Codes.
|
ECAI |
2020 |
5 |
On cognitive preferences and the plausibility of rule-based models.
|
MLJ |
2020 |
0 |
Learning Analogy-Preserving Sentence Embeddings for Answer Selection.
|
CoNLL |
2019 |
7 |
Mending is Better than Ending: Adapting Immutable Classifiers to Nonstationary Environments using Ensembles of Patches.
|
IJCNN |
2019 |
0 |
Deep Ordinal Reinforcement Learning.
|
ECML/PKDD |
2019 |
5 |
Learning Context-dependent Label Permutations for Multi-label Classification.
|
ICML |
2019 |
11 |
Patching Deep Neural Networks for Nonstationary Environments.
|
IJCNN |
2019 |
2 |
Beta Distribution Drift Detection for Adaptive Classifiers.
|
ESANN |
2019 |
0 |
Batchwise Patching of Classifiers.
|
AAAI |
2018 |
21 |
Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules.
|
PAKDD |
2018 |
6 |
Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification.
|
NIPS/NeurIPS |
2017 |
131 |
A Survey of Preference-Based Reinforcement Learning Methods.
|
JMLR |
2017 |
0 |
Using semantic similarity for multi-label zero-shot classification of text documents.
|
ESANN |
2016 |
31 |
Model-Free Preference-Based Reinforcement Learning.
|
AAAI |
2016 |
72 |
All-in Text: Learning Document, Label, and Word Representations Jointly.
|
AAAI |
2016 |
54 |
Beyond Centrality and Structural Features: Learning Information Importance for Text Summarization.
|
CoNLL |
2016 |
15 |
What Makes Word-level Neural Machine Translation Hard: A Case Study on English-German Translation.
|
COLING |
2016 |
4 |
Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization.
|
COLING |
2016 |
11 |
Editorial.
|
DMKD |
2015 |
0 |
Predicting Unseen Labels Using Label Hierarchies in Large-Scale Multi-label Learning.
|
ECML/PKDD |
2015 |
14 |
Separating Rule Refinement and Rule Selection Heuristics in Inductive Rule Learning.
|
ECML/PKDD |
2014 |
15 |
Graded Multilabel Classification by Pairwise Comparisons.
|
ICDM |
2014 |
17 |
Efficient implementation of class-based decomposition schemes for Naïve Bayes.
|
MLJ |
2014 |
0 |
Large-Scale Multi-label Text Classification - Revisiting Neural Networks.
|
ECML/PKDD |
2014 |
0 |
EPMC: Every Visit Preference Monte Carlo for Reinforcement Learning.
|
ACML |
2013 |
13 |
Editorial: Preference learning and ranking.
|
MLJ |
2013 |
8 |
Preference-based reinforcement learning: a formal framework and a policy iteration algorithm.
|
MLJ |
2012 |
107 |
Efficient prediction algorithms for binary decomposition techniques.
|
DMKD |
2012 |
0 |
Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning.
|
ECML/PKDD |
2011 |
42 |
Heuristic Rule-Based Regression via Dynamic Reduction to Classification.
|
IJCAI |
2011 |
25 |
On the quest for optimal rule learning heuristics.
|
MLJ |
2010 |
76 |
Guest Editorial: Global modeling using local patterns.
|
DMKD |
2010 |
16 |
Binary Decomposition Methods for Multipartite Ranking.
|
ECML/PKDD |
2009 |
79 |
Efficient Decoding of Ternary Error-Correcting Output Codes for Multiclass Classification.
|
ECML/PKDD |
2009 |
11 |
A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning.
|
SDM |
2009 |
0 |
Efficient voting prediction for pairwise multilabel classification.
|
ESANN |
2009 |
0 |
Pairwise learning of multilabel classifications with perceptrons.
|
IJCNN |
2008 |
39 |
Label ranking by learning pairwise preferences.
|
Artificial Intelligence |
2008 |
533 |
Multilabel classification via calibrated label ranking.
|
MLJ |
2008 |
785 |
Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain.
|
ECML/PKDD |
2008 |
157 |
On Meta-Learning Rule Learning Heuristics.
|
ICDM |
2007 |
13 |
On Pairwise Naive Bayes Classifiers.
|
ECML/PKDD |
2007 |
33 |
On Minimizing the Position Error in Label Ranking.
|
ECML/PKDD |
2007 |
15 |
Efficient Pairwise Classification.
|
ECML/PKDD |
2007 |
109 |
A Unified Model for Multilabel Classification and Ranking.
|
ECAI |
2006 |
124 |
Machine learning and games.
|
MLJ |
2006 |
53 |
ROC 'n' Rule Learning - Towards a Better Understanding of Covering Algorithms.
|
MLJ |
2005 |
0 |
An Analysis of Stopping and Filtering Criteria for Rule Learning.
|
ECML/PKDD |
2004 |
15 |
An Analysis of Rule Evaluation Metrics.
|
ICML |
2003 |
114 |
Pairwise Preference Learning and Ranking.
|
ECML/PKDD |
2003 |
235 |
Round Robin Classification.
|
JMLR |
2002 |
490 |
Pairwise Classification as an Ensemble Technique.
|
ECML/PKDD |
2002 |
53 |
Detecting Temporal Change in Event Sequences: An Application to Demographic Data.
|
ECML/PKDD |
2001 |
27 |
Round Robin Rule Learning.
|
ICML |
2001 |
71 |
Learning to Use Operational Advice.
|
ECAI |
2000 |
5 |
Integrative Windowing.
|
JAIR |
1998 |
61 |
Pruning Algorithms for Rule Learning.
|
MLJ |
1997 |
128 |
Noise-Tolerant Windowing.
|
IJCAI |
1997 |
10 |
Digging for Peace: Using Machine Learning Methods for Assessing International Conflict Databases.
|
ECAI |
1996 |
13 |
A Tight Integration of Pruning and Learning (Extended Abstract).
|
ECML/PKDD |
1995 |
6 |
Top-Down Pruning in Relational Learning.
|
ECAI |
1994 |
17 |
Incremental Reduced Error Pruning.
|
ICML |
1994 |
444 |
FOSSIL: A Robust Relational Learner.
|
ECML/PKDD |
1994 |
49 |