Ricardo Vilalta

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Learning Abstract Task Representations pdf

Mikhail M. Meskhi, Adriano Rivolli, Rafael G. Mantovani, 2021, ArXiv.

Conceptual Domain Adaptation Using Deep Learning pdf

Ricardo Vilalta, Behrang Mehrparvar, 2018, ArXiv.

Exploring the spectroscopic diversity of type Ia supernovae with Deep Learning and Unsupervised Clustering

Ricardo Vilalta, Fabian Gieseke, Emille E. O. Ishida, 2006, Proceedings of the International Astronomical Union.

Automated supernova Ia classification using adaptive learning techniques

Ricardo Vilalta, Emille E. O. Ishida, Rafael S. de Souza, 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

A Conceptual Study of Model Selection in Classification - Multiple Local Models vs One Global Model

Ricardo Vilalta, Francisco Ocegueda-Hernandez, C. Bagaria, 2010, ICAART.

Automatic detection of craters in planetary images: an embedded framework using feature selection and boosting

Ricardo Vilalta, Wei Ding, Tomasz F. Stepinski, 2010, CIKM.

Automatic Recognition of Landforms on Mars Using Terrain Segmentation and Classification

Soumya Ghosh, Ricardo Vilalta, Tomasz F. Stepinski, 2006, Discovery Science.

A Perspective View and Survey of Meta-Learning

Ricardo Vilalta, Youssef Drissi, R. Vilalta, 2002, Artificial Intelligence Review.

A Unified Framework for Evaluation Metrics in Classification Using Decision Trees

Ricardo Vilalta, Irina Rish, Mark Brodie, 2001, ECML.

Rule Induction of Computer Events

Joseph L. Hellerstein, Ricardo Vilalta, Sheng Ma, 2001, DSOM.

Using clustering to learn distance functions for supervised similarity assessment

Christoph F. Eick, Ricardo Vilalta, Alain Rouhana, 2005, Eng. Appl. Artif. Intell..

Introduction to the Special Issue on Meta-Learning

Ricardo Vilalta, Pavel Brazdil, Christophe G. Giraud-Carrier, 2004, Machine Learning.

Predicting rare events in temporal domains

Ricardo Vilalta, Sheng Ma, R. Vilalta, 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

DRACULA: Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy

Ricardo Vilalta, Emille E. O. Ishida, Vinicius C. Busti, 2015 .

Operational Data Analysis: Improved Predictions Using Multi-computer Pattern Detection

Ricardo Vilalta, Sholom M. Weiss, Chidanand Apté, 2000, DSOM.

Market Basket Analysis

Johannes Fürnkranz, Luís Torgo, Thomas Stützle, 2010, Encyclopedia of Machine Learning.

A Quantification of Cluster Novelty with an Application to Martian Topography

Ricardo Vilalta, Francisco Ocegueda-Hernandez, Tomasz F. Stepinski, 2004, PKDD.

A hierarchical approach to classification for systems with complex low-level interactions

Ricardo Vilalta, M. Achari, 2003, Proceedings of the 2003 IEEE International Symposium on Intelligent Control.

A unified framework for reinforcement learning, co-learning and meta-learning how to coordinate in collaborative multi-agent systems

Ricardo Vilalta, Predrag T. Tosic, 2010, ICCS.

Using Clustering to Learn Distance Functions for Supervised Similarity Assessment

Christoph F. Eick, Ricardo Vilalta, Alain Rouhana, 2005, MLDM.

Evaluation Metrics in Classification: A Quantification of Distance‐Bias

Ricardo Vilalta, Daniel Oblinger, 2003, Comput. Intell..

Transfer Learning in Astronomy: A New Machine-Learning Paradigm pdf

Ricardo Vilalta, R. Vilalta, 2018, Journal of Physics: Conference Series.

Critical event prediction for proactive management in large-scale computer clusters

Anand Sivasubramaniam, José E. Moreira, Ricardo Vilalta, 2003, KDD '03.

A Data Complexity Approach to Kernel Selection for Support Vector Machines

Ricardo Vilalta, Roberto Valerio, 2014, AAAI.

A Characterization of Difficult Problems in Classification

Ricardo Vilalta, Youssef Drissi, 2002, ICMLA.

Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era pdf

Lior Shamir, Ricardo Vilalta, Yen-Chi Chen, 2019, ArXiv.

Metalearning - Applications to Data Mining

Ricardo Vilalta, Pavel Brazdil, Christophe G. Giraud-Carrier, 2008, Cognitive Technologies.

A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes

Ricardo Vilalta, Irina Rish, R. Vilalta, 2003, ECML.

Fault resilient physical neural networks on a single chip

Ricardo Vilalta, Weidong Shi, Lei Xu, 2014, 2014 International Conference on Compilers, Architecture and Synthesis for Embedded Systems (CASES).

Mean Squared Error

Johannes Fürnkranz, Luís Torgo, Thomas Stützle, 2010, Encyclopedia of Machine Learning.

Table of Contents ICTAI 2005

Christoph F. Eick, Ricardo Vilalta, Adam Belloum, 2005 .

Digital topography models for Martian surfaces

Ricardo Vilalta, Tomasz F. Stepinski, R. Vilalta, 2005, IEEE Geoscience and Remote Sensing Letters.

Supervised Learning to Detect Salt Body

Ricardo Vilalta, Pablo Guillen, Dainis Boumber, 2015 .

Integrating Feature Construction with Multiple Classifiers in Decision Tree Induction

Larry A. Rendell, Ricardo Vilalta, L. Rendell, 1997, ICML.

Piece-Wise Model Fitting Using Local Data Patterns

Christoph F. Eick, Ricardo Vilalta, Murali-Krishna Achari, 2004, ECAI.

A Machine Learning Approach for Dark-Matter Particle Identification Under Extreme Class Imbalance

Ricardo Vilalta, Raymond Sutrisno, Andrew Renshaw, 2019 .

A Framework for Multi-Objective Clustering and Its Application to Co-Location Mining

Christoph F. Eick, Ricardo Vilalta, Rachsuda Jiamthapthaksin, 2009, ADMA.

Multistrategy Ensemble Learning

Johannes Fürnkranz, Luís Torgo, Thomas Stützle, 2010, Encyclopedia of Machine Learning.

Filter-Based Information-Theoretic Feature Selection

Ricardo Vilalta, Farinaz Pisheh, R. Vilalta, 2019, ICAAI.

Development of Pattern Recognition Software for Tracks of Ionizing Radiation In Medipix2-Based (TimePix) Pixel Detector Devices

Ricardo Vilalta, S Kuchibhotla, R Valerio, 2011 .

Non-PC Reviewers

Noriaki Izumi, Xiaohua Hu, Vasa Curcin, 2005, ICDM.

Using Meta-Learning to Support Data Mining

Ricardo Vilalta, Pavel Brazdil, Christophe G. Giraud-Carrier, 2004, Int. J. Comput. Sci. Appl..

Star Classification Under Data Variability: An Emerging Challenge in Astroinformatics

Ricardo Vilalta, Ashish Mahabal, Kinjal Dhar Gupta, 2015, ECML/PKDD.

Analysis of correlation between pediatric asthma exacerbation and exposure to pollutant mixtures with association rule mining

Ricardo Vilalta, Daniel Price, Peggy Lindner, 2016, Artif. Intell. Medicine.

Multi-Armed Bandit

Johannes Fürnkranz, Luís Torgo, Katharina Morik, 2010, Encyclopedia of Machine Learning.

Missing Values

Johannes Fürnkranz, Luís Torgo, Thomas Stützle, 2010, Encyclopedia of Machine Learning.

Machine Learning Tools for Automatic Mapping of Martian Landforms

Soumya Ghosh, Ricardo Vilalta, Tomasz F. Stepinski, 2007, IEEE Intelligent Systems.

A Classification Approach for Prediction of Target Events in Temporal Sequences

Ricardo Vilalta, Sheng Ma, Carlotta Domeniconi, 2002, PKDD.

Machine Learning Tools for Geomorphic Mapping of Planetary Surfaces

Ricardo Vilalta, Tomasz F. Stepinski, 2010 .

Learning and Meta-Learning for Coordination of Autonomous Unmanned Vehicles - A Preliminary Analysis

Ricardo Vilalta, Predrag T. Tosic, R. Vilalta, 2010, ECAI.

Adaptive Clustering : Better Representatives with Reinforcement Learning

Christoph F. Eick, Ricardo Vilalta, Abraham Bagherjeiran, 2005 .

Inductive Transfer

Ricardo Vilalta, Carlos Soares, Pavel Brazdil, 2010, Encyclopedia of Machine Learning.

Modeling repetitive patterns: A bridge between pattern theory and data mining

Ricardo Vilalta, Luis Real, R. Vilalta, 2012, 2012 IEEE International Conference on Granular Computing.

Inductive Transfer

Ricardo Vilalta, Carlos Soares, Pavel Brazdil, 2010, Encyclopedia of Machine Learning.

Content-based image retrieval through a multi-agent meta-learning framework

Christoph F. Eick, Ricardo Vilalta, Abraham Bagherjeiran, 2005, 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05).

Predictive algorithms in the management of computer systems

Joseph L. Hellerstein, Ricardo Vilalta, Sheng Ma, 2002, IBM Syst. J..

Tablet PC video based hybrid coursework in computer science: report from a pilot project

Venkat Subramaniam, Ricardo Vilalta, Jaspal Subhlok, 2007, SIGCSE '07.

Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients

Ricardo Vilalta, Mi Dai, Emille E. O. Ishida, 2020, 2020 IEEE Symposium Series on Computational Intelligence (SSCI).

Perinasal indicators of deceptive behavior

Judee K. Burgoon, Ricardo Vilalta, Dvijesh Shastri, 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

Understanding Accuracy Performance Through Concept Characterization and Algorithm Analysis

Ricardo Vilalta, 1999 .

Subkilometer crater discovery with boosting and transfer learning

Xindong Wu, Ricardo Vilalta, Wei Ding, 2011, TIST.

Disk failure prediction in heterogeneous environments

Albert Mo Kim Cheng, Ricardo Vilalta, Darrell D. E. Long, 2017, 2017 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS).

Cluster Validation

Ricardo Vilalta, Tomasz F. Stepinski, R. Vilalta, 2009, Encyclopedia of Data Warehousing and Mining.

Robust Authorship Verification with Transfer Learning

Ricardo Vilalta, Yifan Zhang, Marjan Hosseinia, 2019, EasyChair Preprints.

On the development of inductive learning algorithms: generating flexible and adaptable concept representations

Larry A. Rendell, Ricardo Vilalta, L. Rendell, 1998 .

Class decomposition via clustering: a new framework for low-variance classifiers

Christoph F. Eick, Ricardo Vilalta, Murali-Krishna Achari, 2003, Third IEEE International Conference on Data Mining.

Using representative-based clustering for nearest neighbor dataset editing

Christoph F. Eick, Ricardo Vilalta, Nidal M. Zeidat, 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

Domain Adaptation under Data Misalignment: An Application to Cepheid Variable Star Classification

Ricardo Vilalta, Kinjal Dhar Gupta, Lucas Macri, 2014, 2014 22nd International Conference on Pattern Recognition.

Machine Learning for Automatic Mapping of Planetary Surfaces

Soumya Ghosh, Ricardo Vilalta, Tomasz F. Stepinski, 2007, AAAI.

Automatic Annotation of Planetary Surfaces With Geomorphic Labels

Soumya Ghosh, Ricardo Vilalta, Tomasz F. Stepinski, 2010, IEEE Transactions on Geoscience and Remote Sensing.

Photometric redshift estimation: An active learning approach

Ricardo Vilalta, Ashish Mahabal, Emille E. O. Ishida, 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

Testing Theories in Particle Physics Using Maximum Likelihood and Adaptive Bin Allocation

Ricardo Vilalta, Bruce Knuteson, B. Knuteson, 2005, PKDD.

Meta-Learning - Concepts and Techniques

Ricardo Vilalta, Pavel Brazdil, Christophe G. Giraud-Carrier, 2010, Data Mining and Knowledge Discovery Handbook.

Mean Absolute Error

Johannes Fürnkranz, Luís Torgo, Thomas Stützle, 2010, Encyclopedia of Machine Learning and Data Mining.

Inverse Resolution

Pierre Flener, Eamonn J. Keogh, Ricardo Vilalta, 2010, Encyclopedia of Machine Learning and Data Mining.

An efficient approach to external cluster assessment with an application to martian topography

Ricardo Vilalta, Tomasz F. Stepinski, Murali-Krishna Achari, 2007, Data Mining and Knowledge Discovery.

Global Data Analysis and the Fragmentation Problem in Decision Tree Induction

Larry A. Rendell, Ricardo Vilalta, Gunnar Blix, 1997, ECML.

Adaptive clustering: obtaining better clusters using feedback and past experience

Christoph F. Eick, Ricardo Vilalta, Chun-Sheng Chen, 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

Identifying and Characterizing Class Clusters to Explain Learning Performance

Ricardo Vilalta, 2006, AAAI Spring Symposium: What Went Wrong and Why: Lessons from AI Research and Applications.

A General Approach to Domain Adaptation with Applications in Astronomy pdf

Mikhail M. Meskhi, Ricardo Vilalta, Kinjal Dhar Gupta, 2018, Publications of the Astronomical Society of the Pacific.

Data Selection Using SASH Trees for Support Vector Machines

Chaofan Sun, Ricardo Vilalta, R. Vilalta, 2007, MLDM.

A Quantification of Distance Bias Between Evaluation Metrics In Classification

Ricardo Vilalta, Daniel Oblinger, Daniel Oblinger, 2000, ICML.

Applications and Techniques for Fast Machine Learning in Science pdf

Zhiqiang Que, Ricardo Vilalta, Seda Ogrenci Memik, 2021, Frontiers in Big Data.

Kernel Selection in Support Vector Machines Using Gram-Matrix Properties

Ricardo Vilalta, Roberto Valerio, 2014 .

Probing the Fundamental Nature of Dark Matter with the Large Synoptic Survey Telescope

Nathan Golovich, Ricardo Vilalta, Esra Bulbul, 2019, 1902.01055.

A machine learning approach to Cepheid variable star classification using data alignment and maximum likelihood

Ricardo Vilalta, Kinjal Dhar Gupta, Lucas M. Macri, 2013, Astron. Comput..

Detecting Impact Craters in Planetary Images Using Machine Learning

Ricardo Vilalta, Wei Ding, Tomasz F. Stepinski, 2012 .

An Empirical Study of the Suitability of Class Decomposition for Linear Models: When Does It Work Well?

Ricardo Vilalta, Francisco Ocegueda-Hernandez, 2013, SDM.

Dark Matter Science in the Era of LSST

Benjamin Rose, Julian Borrill, Adam D. Myers, 2019, 1903.04425.

The Value of Lookahead Feature Construction in Decision Tree Induction

Ricardo Vilalta, Gunnar Blix, Larry Rendell, 1995 .

Mnemotechnics in Second-Language Learning

Stephan Lewandowsky, In So Kweon, Dariusz Plewczynski, 2012 .

On the Importance of Change of Representation in Induction

Ricardo Vilalta, R. Vilalta, Larry A. Rendellperez, 1996 .

THEMATIC MAPS OF MARTIAN TOPOGRAPHY GENERATED BY A CLUSTERING ALGORITHM

Ricardo Vilalta, 2004 .