A. Airola

发表

Learning to Rank with Pairwise Regularized Least-Squares

T. Salakoski, J. Boberg, T. Pahikkala, 2007 .

A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines.

Evan O. Paull, Joshua M. Stuart, Adam A. Margolin, 2017, Cell systems.

Regularized Machine Learning in the Genetic Prediction of Complex Traits

T. Salakoski, T. Aittokallio, S. Ripatti, 2014, PLoS genetics.

Linear Time Feature Selection for Regularized Least-Squares

T. Salakoski, T. Pahikkala, A. Airola, 2010, 1003.3570.

Feature selection for regularized least-squares: New computational short-cuts and fast algorithmic implementations

Tapio Salakoski, Tapio Pahikkala, Antti Airola, 2010, 2010 IEEE International Workshop on Machine Learning for Signal Processing.

On remote matching of ships to pollution

A. Airola, P. Nevalainen, T. Yliopisto, 2019 .

Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review

Amir M. Rahmani, A. Airola, Zahra Sharifiheris, 2021, JMIR medical informatics.

Large scale training methods for linear RankRLS

Tapio Salakoski, Tapio Pahikkala, Antti Airola, 2010 .

Assessment of metal ion concentration in water with structured feature selection.

T. Salakoski, T. Pahikkala, A. Airola, 2017, Chemosphere.

Resting state EEG as a biomarker of Parkinson’s disease: Influence of measurement conditions

H. Railo, T. Pahikkala, A. Airola, 2020, bioRxiv.

Classification of Atrial Fibrillation and Acute Decompensated Heart Failure Using Smartphone Mechanocardiography: A Multilabel Learning Approach

Mojtaba Jafari Tadi, O. Lahdenoja, J. Airaksinen, 2020, IEEE Sensors Journal.

Prostate Cancer Risk Stratification in Men With a Clinical Suspicion of Prostate Cancer Using a Unique Biparametric MRI and Expression of 11 Genes in Apparently Benign Tissue: Evaluation Using Machine‐Learning Techniques

H. Merisaari, P. Taimen, P. Boström, 2020, Journal of magnetic resonance imaging : JMRI.

Propagating AI Knowledge Across University Disciplines- The Design of A Multidisciplinary AI Study Module

Tapio Salakoski, Antti Airola, Ali Farooq, 2020, 2020 IEEE Frontiers in Education Conference (FIE).

Conditional ranking algorithms for efﬁcient object retrieval and object querying on relational data

Tapio Salakoski, Bernard De Baets, Tapio Pahikkala, 2012 .

Learning Multi-Label Predictors under Sparsity Budget

Tapio Salakoski, Tapio Pahikkala, Antti Airola, 2011, SCAI.

Predicting patient acuity from electronic patient records

Tapio Salakoski, Heljä Lundgrén-Laine, Sanna Salanterä, 2014, J. Biomed. Informatics.

How engineers perceive the importance of ethics in Finland

T. Pahikkala, E. Sutinen, A. Airola, 2018 .

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data.

Thomas Yu, Tapio Pahikkala, Antti Airola, 2017, The Lancet. Oncology.

Quicksort leave-pair-out cross-validation for ROC curve analysis

T. Pahikkala, I. Jambor, A. Airola, 2022, Computational Statistics.

DREAM Community. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial

Stephen R. Piccolo, Suleiman A. Khan, Luke N. Kumar, 2016 .

The spatial leave-pair-out cross-validation method for reliable AUC estimation of spatial classifiers

J. Heikkonen, T. Pahikkala, A. Airola, 2019, Data Mining and Knowledge Discovery.

Efficient regularized least-squares algorithms for conditional ranking on relational data

B. Baets, Michiel Stock, T. Pahikkala, 2012, Machine Learning.

Wrapper-based selection of genetic features in genome-wide association studies through fast matrix operations

T. Salakoski, T. Aittokallio, T. Pahikkala, 2012, Algorithms for Molecular Biology.

Efficient recurrent local search strategies for semi- and unsupervised regularized least-squares classification

F. Gieseke, Oliver Kramer, T. Pahikkala, 2012, Evolutionary Intelligence.

A Kernel-Based Framework for Learning Graded Relations From Data

Tapio Salakoski, Bernard De Baets, Tapio Pahikkala, 2011, IEEE Transactions on Fuzzy Systems.

Learning Valued Relations from Data

Tapio Salakoski, Bernard De Baets, Tapio Pahikkala, 2011, Eurofuse.

Efficient recurrent local search strategies for semi- and unsupervised regularized least-squares classification

Oliver Kramer, Tapio Pahikkala, Antti Airola, 2012, Evol. Intell..

Algebraic Shortcuts for Leave-One-Out Cross-Validation in Supervised Network Inference

Tapio Pahikkala, Antti Airola, Michiel Stock, 2018, bioRxiv.

Identification of Functionally Related Enzymes by Learning-to-Rank Methods

Eyke Hüllermeier, Gerhard Klebe, Bernard De Baets, 2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

EXTRACTING CONTEXTUALIZED COMPLEX BIOLOGICAL EVENTS WITH RICH GRAPH‐BASED FEATURE SETS

Jari Björne, Tapio Salakoski, Juho Heimonen, 2011, Comput. Intell..

Toward more realistic drug^target interaction predictions

Tapio Pahikkala, Antti Airola, Tero Aittokallio, 2014 .

Higher Education during Lockdown: Literature Review and Implications on Technology Design

A. Airola, Samuli Laato, M. Murtonen, 2022, Education Research International.

Extracting Complex Biological Events with Rich Graph-Based Feature Sets

Jari Björne, Tapio Salakoski, Juho Heimonen, 2009, BioNLP@HLT-NAACL.

An experimental comparison of cross-validation techniques for estimating the area under the ROC curve

Tapio Salakoski, Bernard De Baets, Tapio Pahikkala, 2011, Comput. Stat. Data Anal..

Tournament leave-pair-out cross-validation for receiver operating characteristic analysis

Tapio Pahikkala, Antti Airola, Ivan Jambor, 2018, Statistical methods in medical research.

All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning

Jari Björne, T. Salakoski, Filip Ginter, 2008, BMC Bioinformatics.

Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors

Juho Rousu, Anna Cichonska, Balaguru Ravikumar, 2017, PLoS Comput. Biol..

Detecting Aortic Stenosis Using Seismocardiography and Gryocardiography Combined with Convolutional Neural Networks

O. Lahdenoja, A. Airola, M. Savontaus, 2021, 2021 Computing in Cardiology (CinC).

Adaptive risk prediction system with incremental and transfer learning

Tapio Pahikkala, Antti Airola, Aki Koivu, 2021, Comput. Biol. Medicine.

A comparison of AUC estimators in small-sample studies

Tapio Salakoski, Bernard De Baets, Tapio Pahikkala, 2009, MLSB.

Comparative analysis of five protein-protein interaction corpora

Jari Björne, Tapio Salakoski, Juho Heimonen, 2008, BMC Bioinformatics.

Cost-effective survival prediction for patients with advanced prostate cancer using clinical trial and real-world hospital registry datasets

Tapio Pahikkala, Antti Airola, Tero Aittokallio, 2019, Int. J. Medical Informatics.

Learning with multiple pairwise kernels for drug bioactivity prediction

Juho Rousu, Anna Cichonska, Tapio Pahikkala, 2018, Bioinform..

Circulating Cytokines Predict the Development of Insulin Resistance in a Prospective Finnish Population Cohort.

O. Raitakari, T. Pahikkala, S. Jalkanen, 2016, The Journal of clinical endocrinology and metabolism.

Drug-Drug Interaction Extraction from Biomedical Texts with SVM and RLS Classifiers

Tapio Salakoski, Tapio Pahikkala, Antti Airola, 2011 .

Fast and simple gradient-based optimization for semi-supervised support vector machines

Oliver Kramer, Tapio Pahikkala, Antti Airola, 2014, Neurocomputing.

A Low-Overhead, Fully-Distributed, Guaranteed-Delivery Routing Algorithm for Faulty Network-on-Chips

Hannu Tenhunen, Siamak Mohammadi, Pasi Liljeberg, 2015, NOCS.

Robust ECG R-peak detection using LSTM

Mingzhe Jiang, Nikil D. Dutt, Pasi Liljeberg, 2020, SAC.

Multi-label learning under feature extraction budgets

Tapio Salakoski, Tapio Pahikkala, Antti Airola, 2014, Pattern Recognit. Lett..

A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression

Bernard De Baets, Tapio Pahikkala, Antti Airola, 2018, Neural Computation.

A kernel framework for learning relations from paired-comparison data

Tapio Salakoski, Bernard De Baets, Tapio Pahikkala, 2011 .

Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method pdf

Bernard De Baets, Tapio Pahikkala, Antti Airola, 2016, ArXiv.

A Two-Step Learning Approach for Solving Full and Almost Full Cold Start Problems in Dyadic Prediction

Bernard De Baets, Tapio Pahikkala, Antti Airola, 2014, ECML/PKDD.

A general-purpose toolbox for efficient Kronecker-based learning

Bernard De Baets, Tapio Pahikkala, Antti Airola, 2020 .

Comparison of automatic summarisation methods for clinical free text notes

Tapio Salakoski, Juho Heimonen, Sanna Salanterä, 2016, Artif. Intell. Medicine.

Synthetic minority oversampling of vital statistics data with generative adversarial networks

Tapio Pahikkala, Antti Airola, Aki Koivu, 2020, J. Am. Medical Informatics Assoc..

On Evaluation of Automatically Generated Clinical Discharge Summaries

Tapio Salakoski, Juho Heimonen, Laura-Maria Murtola, 2014, PAHI.

Multichannel Bed Based Ballistocardiography Heart Rate Estimation Using Continuous Wavelet Transforms and Autocorrelation

O. Lahdenoja, A. Airola, T. Koivisto, 2022, 2022 Computing in Cardiology (CinC).

A Graph Kernel for Protein-Protein Interaction Extraction

Jari Björne, Tapio Salakoski, Sampo Pyysalo, 2008, BioNLP.

Fast and parallelized greedy forward selection of genetic variants in Genome-wide association studies

Tapio Salakoski, Tapio Pahikkala, Antti Airola, 2011, 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS).

Speeding Up Greedy Forward Selection for Regularized Least-Squares

Tapio Salakoski, Tapio Pahikkala, Antti Airola, 2010, 2010 Ninth International Conference on Machine Learning and Applications.

Fast Kronecker Product Kernel Methods via Generalized Vec Trick

Tapio Pahikkala, Antti Airola, T. Pahikkala, 2016, IEEE Transactions on Neural Networks and Learning Systems.

Fast Kronecker product kernel methods via sampled vec trick pdf

Tapio Pahikkala, Antti Airola, T. Pahikkala, 2016, ArXiv.

On Learning and Cross-Validation with Decomposed Nyström Approximation of Kernel Matrix

Tapio Salakoski, Tapio Pahikkala, Antti Airola, 2011, Neural Processing Letters.

Comparative analysis of five protein-protein interaction corpora

Jari Björne, T. Salakoski, Filip Ginter, 2008, BMC bioinformatics.

Sparse Quasi-Newton Optimization for Semi-supervised Support Vector Machines

Oliver Kramer, Tapio Pahikkala, Antti Airola, 2012, ICPRAM.

An efficient algorithm for learning to rank from preference graphs

Tapio Pahikkala, Antti Airola, Jouni Järvinen, 2009, Machine Learning.

Learning Low Cost Multi-target Models by Enforcing Sparsity

Tapio Salakoski, Tapio Pahikkala, Antti Airola, 2015, IEA/AIE.

Parallel Feature Selection for Regularized Least-Squares

Tapio Salakoski, Tapio Pahikkala, Antti Airola, 2012, PARA.

Training linear ranking SVMs in linearithmic time using red-black trees

Tapio Salakoski, Tapio Pahikkala, Antti Airola, 2010, Pattern Recognit. Lett..

Implementation and Analysis of Block Dense Matrix Decomposition on Network-on-Chips

Tapio Salakoski, Hannu Tenhunen, Thomas Canhao Xu, 2012, 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems.

Modelling user retention in mobile games

Jukka Heikkonen, Tapio Pahikkala, Antti Airola, 2016, 2016 IEEE Conference on Computational Intelligence and Games (CIG).

Tournament leave-pair-out cross-validation for receiver operating characteristic analysis

P. Boström, T. Pahikkala, I. Jambor, 2018, Statistical methods in medical research.

Measuring Player Retention and Monetization Using the Mean Cumulative Function

Tapio Pahikkala, Antti Airola, Jukka Heikkonen, 2017, IEEE Transactions on Games.

Wrapper-based selection of genetic features in genome-wide association studies through fast matrix operations

Tapio Salakoski, Tapio Pahikkala, Antti Airola, 2012, Algorithms for Molecular Biology.

Playtime Measurement With Survival Analysis

Tapio Pahikkala, Antti Airola, Jukka Heikkonen, 2017, IEEE Transactions on Games.

User activity decay in mobile games determined by simple differential equations?

Jukka Heikkonen, Tapio Pahikkala, Antti Airola, 2016, 2016 IEEE Conference on Computational Intelligence and Games (CIG).

Conditional Ranking on Relational Data

Tapio Salakoski, Bernard De Baets, Tapio Pahikkala, 2010, ECML/PKDD.

Comparison of automatic summarisation methods for clinical free text notes

T. Salakoski, S. Salanterä, T. Pahikkala, 2016, Artif. Intell. Medicine.

Accelerating Image Processing Using Reduced Precision Calculation Convolution Engines

Olli I. Heimo, A. Airola, T. Säntti, 2023, Journal of Signal Processing Systems.

An Improved Training Algorithm for the Linear Ranking Support Vector Machine

Tapio Salakoski, Tapio Pahikkala, Antti Airola, 2011, ICANN.

J. Boberg, T. Pahikkala, Evgeni Tsivtsivadze, 2020, Data Mining and Machine Learning.

Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?

T. Pahikkala, A. Airola, Valtteri Nieminen, 2024, 2403.13612.

Evaluating Classifiers Trained on Differentially Private Synthetic Health Data

T. Pahikkala, A. Airola, Valtteri Nieminen, 2023, 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS).

Toward more realistic drug–target interaction predictions

T. Aittokallio, T. Pahikkala, A. Airola, 2014, Briefings Bioinform..

Budget-Based Classification of Parkinson's Disease From Resting State EEG

T. Pahikkala, A. Airola, V. Kaasinen, 2023, IEEE Journal of Biomedical and Health Informatics.

Domain randomization using synthetic electrocardiograms for training neural networks

A. Airola, Juho Laitala, M. Kaisti, 2023, Artif. Intell. Medicine.

Training neural networks with synthetic electrocardiograms pdf

Antti Airola, Matti Kaisti, Juho Laitala, 2021, ArXiv.

Efficient regularized least-squares algorithms for conditional ranking on relational data

Bernard De Baets, Tapio Pahikkala, Antti Airola, 2012, Machine Learning.