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Jaakko Hollmén
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- affiliation: Aalto University, Finland
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2020 – today
- 2024
- [c75]Guilherme Dinis Junior
, Sindri Magnússon
Policy Control with Delayed, Aggregate, and Anonymous Feedback. ECML/PKDD (6) 2024: 389-406 - 2023
- [c74]Mahbub Ul Alam
COVID-19 detection from thermal image and tabular medical data utilizing multi-modal machine learning. CBMS 2023: 646-653 - [c73]Maria Movin
Explaining Black Box Reinforcement Learning Agents Through Counterfactual Policies. IDA 2023: 314-326 - [i5]Serio Agriesti, Vladimir Kuzmanovski, Jaakko Hollmén, Claudio Roncoli, Bat-hen Nahmias-Biran:
A Bayesian Optimization approach for calibrating large-scale activity-based transport models. CoRR abs/2302.03480 (2023) - [i4]Mahbub Ul Alam
SHAMSUL: Simultaneous Heatmap-Analysis to investigate Medical Significance Utilizing Local interpretability methods. CoRR abs/2307.08003 (2023) - 2022
- [c72]Lena Mondrejevski
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction. CBMS 2022: 32-37 - [c71]Guilherme Dinis Junior
Policy Evaluation with Delayed, Aggregated Anonymous Feedback. DS 2022: 114-123 - [c70]Vladimir Kuzmanovski
Semi-parametric Approach to Random Forests for High-Dimensional Bayesian Optimisation. DS 2022: 418-428 - [c69]Miki Sirola, Olli-Pekka Rinta-Koski, Le Ngu Nguyen, Jaakko Hollmén:
Principal Component Analysis Visualizations in State Discovery by Animating Exploration Results. SMARTCOMP 2022: 257-262 - [i3]Lena Mondrejevski, Ioanna Miliou
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction. CoRR abs/2205.15104 (2022) - 2021
- [c68]Olli-Pekka Rinta-Koski, Miki Sirola, Le Ngu Nguyen, Jaakko Hollmén:
State Discovery and Prediction from Multivariate Sensor Data. AALTD@ECML/PKDD 2021: 155-169 - [c67]Luis Quintero
Effective Classification of Head Motion Trajectories in Virtual Reality Using Time-Series Methods. AIVR 2021: 38-46 - [c66]Vladimir Kuzmanovski
Composite Surrogate for Likelihood-Free Bayesian Optimisation in High-Dimensional Settings of Activity-Based Transportation Models. IDA 2021: 171-183 - 2020
- [j17]Joel Jaskari
Machine Learning Methods for Neonatal Mortality and Morbidity Classification. IEEE Access 8: 123347-123358 (2020) - [c65]Maria Bampa, Panagiotis Papapetrou, Jaakko Hollmén:
A Clustering Framework for Patient Phenotyping with Application to Adverse Drug Events. CBMS 2020: 177-182 - [c64]Emma Briggs
Mitigating Discrimination in Clinical Machine Learning Decision Support Using Algorithmic Processing Techniques. DS 2020: 19-33
2010 – 2019
- 2019
- [c63]Vladimir Kuzmanovski
Temporal Analysis of Adverse Weather Conditions Affecting Wheat Production in Finland. DS 2019: 176-185 - [c62]Jaakko Hollmén, Panagiotis Papapetrou:
Clustering Diagnostic Profiles of Patients. AIAI 2019: 120-126 - 2018
- [j16]Olli-Pekka Rinta-Koski
Gaussian process classification for prediction of in-hospital mortality among preterm infants. Neurocomputing 298: 134-141 (2018) - [j15]Roelant A. Stegmann, Indre Zliobaite
A survey of evaluation methods for personal route and destination prediction from mobility traces. WIREs Data Mining Knowl. Discov. 8(2) (2018) - [c61]Jaakko Hollmén, Lars Asker, Isak Karlsson, Panagiotis Papapetrou, Henrik Boström, Birgitta Norstedt Wikner, Inger Öhman
Exploring epistaxis as an adverse effect of anti-thrombotic drugs and outdoor temperature. PETRA 2018: 1-4 - [c60]Teemu Lehto, Markku Hinkka, Jaakko Hollmén:
Analyzing Business Process Changes Using Influence Analysis. SIMPDA 2018: 32-46 - [e6]Jaakko Hollmén, Carolyn McGregor, Paolo Soda, Bridget Kane:
31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018, Karlstad, Sweden, June 18-21, 2018. IEEE Computer Society 2018, ISBN 978-1-5386-6060-7 [contents] - 2017
- [j14]Jukka Teittinen, Markus Hiienkari, Indre Zliobaite
A 5.3 pJ/op approximate TTA VLIW tailored for machine learning. Microelectron. J. 61: 106-113 (2017) - [j13]Jesse Read, Luca Martino
Multi-label methods for prediction with sequential data. Pattern Recognit. 63: 45-55 (2017) - [c59]Olli-Pekka Rinta-Koski, Simo Särkkä, Jaakko Hollmén, Markus Leskinen, Sture Andersson:
Prediction of preterm infant mortality with Gaussian process classification. ESANN 2017 - [c58]Alexandr V. Maslov, Mykola Pechenizkiy
BLPA: Bayesian learn-predict-adjust method for online detection of recurrent changepoints. IJCNN 2017: 1916-1923 - [c57]Mikko Rinne, Mehrdad Bagheri, Tuukka Tolvanen, Jaakko Hollmén:
Automatic Recognition of Public Transport Trips from Mobile Device Sensor Data and Transport Infrastructure Information. PAP@PKDD/ECML 2017: 76-97 - [c56]Teemu Lehto, Markku Hinkka, Jaakko Hollmén:
Focusing Business Process Lead Time Improvements Using Influence Analysis. SIMPDA 2017: 54-67 - [e5]Michelangelo Ceci, Jaakko Hollmén, Ljupco Todorovski, Celine Vens, Saso Dzeroski:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part I. Lecture Notes in Computer Science 10534, Springer 2017, ISBN 978-3-319-71248-2 [contents] - [e4]Michelangelo Ceci, Jaakko Hollmén, Ljupco Todorovski, Celine Vens, Saso Dzeroski:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part II. Lecture Notes in Computer Science 10535, Springer 2017, ISBN 978-3-319-71245-1 [contents] - 2016
- [j12]Jesse Read, Indre Zliobaite
Labeling sensing data for mobility modeling. Inf. Syst. 57: 207-222 (2016) - [j11]Prem Raj Adhikari, Anze Vavpetic, Jan Kralj, Nada Lavrac, Jaakko Hollmén:
Explaining mixture models through semantic pattern mining and banded matrix visualization. Mach. Learn. 105(1): 3-39 (2016) - [c55]Teemu Lehto, Markku Hinkka, Jaakko Hollmén:
Focusing Business Improvements Using Process Mining Based Influence Analysis. BPM (Forum) 2016: 177-192 - [c54]Daniel Vieira, Jaakko Hollmén:
Resource Frequency Prediction in Healthcare: Machine Learning Approach. CBMS 2016: 88-93 - [i2]Jesse Read, Luca Martino, Jaakko Hollmén:
Multi-label Methods for Prediction with Sequential Data. CoRR abs/1609.08349 (2016) - 2015
- [j10]Prem Raj Adhikari, Jaakko Hollmén:
Fast progressive training of mixture models for model selection. J. Intell. Inf. Syst. 44(2): 223-241 (2015) - [j9]Indre Zliobaite
Optimizing regression models for data streams with missing values. Mach. Learn. 99(1): 47-73 (2015) - [c53]Prem Raj Adhikari, Jaakko Hollmén:
Resolution Transfer in Cancer Classification Based on Amplification Patterns. Discovery Science 2015: 1-8 - [c52]Olli-Pekka Rinta-Koski, Jaakko Hollmén, Markus Leskinen
Variation in oxygen saturation measurements in very low birth weight infants. PETRA 2015: 29:1-29:3 - [i1]Jesse Read, Jaakko Hollmén:
Multi-label Classification using Labels as Hidden Nodes. CoRR abs/1503.09022 (2015) - 2014
- [j8]Indre Zliobaite
Towards Hardware-driven Design of Low-energy Algorithms for Data Analysis. SIGMOD Rec. 43(4): 15-20 (2014) - [c51]Prem Raj Adhikari, Anze Vavpetic, Jan Kralj, Nada Lavrac, Jaakko Hollmén:
Explaining Mixture Models through Semantic Pattern Mining and Banded Matrix Visualization. Discovery Science 2014: 1-12 - [c50]Indre Zliobaite, Jaakko Hollmén:
Mobile Sensing Data for Urban Mobility Analysis: A Case Study in Preprocessing. EDBT/ICDT Workshops 2014: 309-314 - [c49]Konsta Sirvio, Jaakko Hollmén:
Multi-Step Ahead Forecasting of Road Condition Using Least Squares Support Vector Regression. ESANN 2014 - [c48]Jesse Read, Jaakko Hollmén:
A Deep Interpretation of Classifier Chains. IDA 2014: 251-262 - [c47]Panos Sakkos, Dimitrios Kotsakos, Vana Kalogeraki
Defining a mobile architecture for structural health monitoring. PETRA 2014: 64:1-64:4 - 2013
- [c46]Daniel Vieira, Jari Linden, Jaakko Hollmén, Jorma Suni:
Challenges in predicting community periodontal index from hospital dental care records. CBMS 2013: 107-112 - [c45]Prem Raj Adhikari, Jaakko Hollmén:
Mixture Models from Multiresolution 0-1 Data. Discovery Science 2013: 1-16 - [c44]Indre Zliobaite
Fault Tolerant Regression for Sensor Data. ECML/PKDD (1) 2013: 449-464 - 2012
- [j7]Miguel A. Prada
Three-way analysis of structural health monitoring data. Neurocomputing 80: 119-128 (2012) - [j6]Alexios Kotsifakos, Panagiotis Papapetrou, Jaakko Hollmén, Dimitrios Gunopulos
Hum-a-song: A Subsequence Matching with Gaps-Range-Tolerances Query-By-Humming System. Proc. VLDB Endow. 5(12): 1930-1933 (2012) - [c43]Prem Raj Adhikari, Jaakko Hollmén:
Fast Progressive Training of Mixture Models for Model Selection. Discovery Science 2012: 194-208 - [c42]Alexios Kotsifakos, Panagiotis Papapetrou
A survey of query-by-humming similarity methods. PETRA 2012: 5 - [c41]Panagiotis Papapetrou
Finding representative objects using link analysis ranking. PETRA 2012: 6 - [c40]Jaakko Hollmén:
Mixture modeling of gait patterns from sensor data. PETRA 2012: 48 - [c39]Prem Raj Adhikari, Jaakko Hollmén:
Multiresolution Mixture Modeling using Merging of Mixture Components. ACML 2012: 17-32 - [e3]Jaakko Hollmén, Frank Klawonn, Allan Tucker:
Advances in Intelligent Data Analysis XI - 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings. Lecture Notes in Computer Science 7619, Springer 2012, ISBN 978-3-642-34155-7 [contents] - 2011
- [j5]Alexios Kotsifakos, Panagiotis Papapetrou, Jaakko Hollmén, Dimitrios Gunopulos:
A Subsequence Matching with Gaps-Range-Tolerances Framework: A Query-By-Humming Application. Proc. VLDB Endow. 4(11): 761-771 (2011) - [c38]Konsta Sirvio, Jaakko Hollmén:
Forecasting Road Condition after Maintenance Works by Linear Methods and Radial Basis Function Networks. ICANN (2) 2011: 405-412 - [c37]Maurizio Bocca, Janne Toivola, Lasse M. Eriksson, Jaakko Hollmén, Heikki N. Koivo:
Structural Health Monitoring in Wireless Sensor Networks by the Embedded Goertzel Algorithm. ICCPS 2011: 206-214 - [c36]Janne Toivola, Jaakko Hollmén:
Collaborative Filtering for Coordinated Monitoring in Sensor Networks. ICDM Workshops 2011: 987-994 - [c35]Serafín Alonso
Comparative Analysis of Power Consumption in University Buildings Using envSOM. IDA 2011: 10-21 - [c34]Mark J. Brewer, Mika Sulkava, Harri Mäkinen, Mikko Korpela, Pekka Nöjd, Jaakko Hollmén:
Logistic Fitting Method for Detecting Onset and Cessation of Tree Stem Radius Increase. IDEAL 2011: 204-211 - [c33]Alexios Kotsifakos, Vassilis Athitsos, Panagiotis Papapetrou, Jaakko Hollmén, Dimitrios Gunopulos
Model-based search in large time series databases. PETRA 2011: 36 - [c32]Orestis Kostakis, Panagiotis Papapetrou, Jaakko Hollmén:
Distance measure for querying sequences of temporal intervals. PETRA 2011: 40 - [c31]Orestis Kostakis, Panagiotis Papapetrou
ARTEMIS: Assessing the Similarity of Event-Interval Sequences. ECML/PKDD (2) 2011: 229-244 - [c30]Prem Raj Adhikari, Bimal Babu Upadhyaya, Chen Meng
Gene Selection in Time-Series Gene Expression Data. PRIB 2011: 145-156 - [c29]Serafín Alonso
EnvSOM: A SOM Algorithm Conditioned on the Environment for Clustering and Visualization. WSOM 2011: 61-70 - [e2]Tapio Elomaa, Jaakko Hollmén, Heikki Mannila:
Discovery Science - 14th International Conference, DS 2011, Espoo, Finland, October 5-7, 2011. Proceedings. Lecture Notes in Computer Science 6926, Springer 2011, ISBN 978-3-642-24476-6 [contents] - [e1]João Gama
Advances in Intelligent Data Analysis X - 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011. Proceedings. Lecture Notes in Computer Science 7014, Springer 2011, ISBN 978-3-642-24799-6 [contents] - 2010
- [j4]Mikko Korpela, Harri Mäkinen
Automatic detection of onset and cessation of tree stem radius increase using dendrometer data. Neurocomputing 73(10-12): 2039-2046 (2010) - [c28]Janne Toivola, Miguel A. Prada
Novelty Detection in Projected Spaces for Structural Health Monitoring. IDA 2010: 208-219 - [c27]Konsta Sirvio, Jaakko Hollmén:
Multi-year network level road maintenance programming by genetic algorithms and variable neighbourhood search. ITSC 2010: 581-586 - [c26]Jefrey Lijffijt
Tracking your steps on the track: body sensor recordings of a controlled walking experiment. PETRA 2010 - [c25]Jefrey Lijffijt
Benchmarking dynamic time warping for music retrieval. PETRA 2010 - [c24]Prem Raj Adhikari, Jaakko Hollmén:
Preservation of Statistically Significant Patterns in Multiresolution 0-1 Data. PRIB 2010: 86-97
2000 – 2009
- 2009
- [c23]Janne Toivola, Jaakko Hollmén:
Feature Extraction and Selection from Vibration Measurements for Structural Health Monitoring. IDA 2009: 213-224 - 2008
- [j3]Jarkko Tikka, Jaakko Hollmén:
Sequential input selection algorithm for long-term prediction of time series. Neurocomputing 71(13-15): 2604-2615 (2008) - [c22]Mikko Korpela, Harri Mäkinen, Mika Sulkava, Pekka Nöjd, Jaakko Hollmén:
Smoothed Prediction of the Onset of Tree Stem Radius Increase Based on Temperature Patterns. Discovery Science 2008: 100-111 - [c21]Jarkko Tikka, Jaakko Hollmén:
Selection of important input variables for RBF network using partial derivatives. ESANN 2008: 167-172 - [c20]Konsta Sirvio, Jaakko Hollmén:
Spatio-temporal Road Condition Forecasting with Markov Chains and Artificial Neural Networks. HAIS 2008: 204-211 - 2007
- [j2]Mika Sulkava, Sebastiaan Luyssaert
Modeling the effects of varying data quality on trend detection in environmental monitoring. Ecol. Informatics 2(2): 167-176 (2007) - [c19]Jaakko Hollmén:
Model Selection and Estimation Via Subjective User Preferences. Discovery Science 2007: 259-263 - [c18]Robert Gwadera, Janne Toivola, Jaakko Hollmén:
Segmenting Multi-attribute Sequences Using Dynamic Bayesian Networks. ICDM Workshops 2007: 465-470 - [c17]Jaakko Hollmén, Jarkko Tikka:
Compact and Understandable Descriptions of Mixtures of Bernoulli Distributions. IDA 2007: 1-12 - [c16]Jarkko Tikka, Jaakko Hollmén, Samuel Myllykangas:
Mixture Modeling of DNA Copy Number Amplification Patterns in Cancer. IWANN 2007: 972-979 - 2006
- [c15]Antti Rasinen, Jaakko Hollmén, Heikki Mannila:
Analysis of Linux Evolution Using Aligned Source Code Segments. Discovery Science 2006: 209-218 - [c14]Jarkko Tikka, Amaury Lendasse, Jaakko Hollmén:
Analysis of Fast Input Selection: Application in Time Series Prediction. ICANN (2) 2006: 161-170 - 2005
- [c13]Mika Sulkava, Pasi Rautio, Jaakko Hollmén:
Combining Measurement Quality into Monitoring Trends in Foliar Nutrient Concentrations. ICANN (2) 2005: 761-767 - [c12]Peddinti V. Gopalacharyulu
Data integration and visualization system for enabling conceptual biology. ISMB (Supplement of Bioinformatics) 2005: 177-185 - [c11]Jarkko Tikka, Jaakko Hollmén, Amaury Lendasse:
Input Selection for Long-Term Prediction of Time Series. IWANN 2005: 1002-1009 - 2004
- [p1]Artur Bykowski, Jouni K. Seppänen, Jaakko Hollmén:
Model-Independent Bounding of the Supports of Boolean Formulae in Binary Data. Database Support for Data Mining Applications 2004: 234-249 - 2003
- [c10]Jaakko Hollmén, Jouni K. Seppänen, Heikki Mannila:
Mixture Models and Frequent Sets: Combining Global and Local Methods for 0-1 Data. SDM 2003: 289-293 - 2002
- [c9]Salla Ruosaari, Jaakko Hollmén:
Image Analysis for Detecting Faulty Spots from Microarray Images. Discovery Science 2002: 259-266 - [c8]Artur Bykowski, Jouni K. Seppänen, Jaakko Hollmén:
Model-independent Bounding of the Supports of Boolean Formulae in Binary Data. KDID 2002: 20-31 - 2001
- [c7]Juha Vesanto, Jaakko Hollmén:
An Automated Report Generation Tool for the Data Understanding Phase. HIS 2001: 611-625 - 2000
- [b1]Jaakko Hollmén:
User profiling and classification for fraud detection in mobile communications networks. Aalto University, Espoo, Helsinki, Finland, 2000 - [c6]Jaakko Hollmén, Volker Tresp:
A hidden Markov model for metric and event-based data. EUSIPCO 2000: 1-4 - [c5]Jaakko Hollmén, Volker Tresp, Olli Simula:
A learning vector quantization algorithm for probabilistic models. EUSIPCO 2000: 1-4 - [c4]Michal Skubacz, Jaakko Hollmén:
Quantization of Continuous Input Variables for Binary Classification. IDEAL 2000: 42-47
1990 – 1999
- 1999
- [j1]Esa Alhoniemi, Jaakko Hollmén, Olli Simula, Juha Vesanto:
Process Monitoring and Modeling Using the Self-Organizing Map. Integr. Comput. Aided Eng. 6(1): 3-14 (1999) - 1998
- [c3]Michiaki Taniguchi, Michael Haft, Jaakko Hollmén, Volker Tresp:
Fraud detection in communication networks using neural and probabilistic methods. ICASSP 1998: 1241-1244 - [c2]Jaakko Hollmén, Volker Tresp:
Call-Based Fraud Detection in Mobile Communication Networks Using a Hierarchical Regime-Switching Model. NIPS 1998: 889-895 - 1997
- [c1]Olli Simula, Esa Alhoniemi, Jaakko Hollmén, Juha Vesanto:
Analysis of Complex Systems Using the Self-Organizing Map. ICONIP (2) 1997: 1313-1317
Coauthor Index
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