Melanie Hilario
Université de Genève, Computer Science, Faculty Member
- Text Mining, Machine Learning, Applied Ontology, Semantic metamining, Artificial Intelligence, Metamining, and 11 moreFeature Selection, Ontology Development, Classification (Machine Learning), Information Extraction, Statistical machine learning, Data Mining, Metalearning, Computer Science, Ontology Engineering, Data Mining in Bioinformatics, and Ontology-based data miningedit
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Research Interests: Engineering, Algorithms, Artificial Intelligence, Machine Learning, Support Vector Machines, and 10 moreHumans, Infection Control, Artificial Intelligence in Medicine, Cluster Analysis, Switzerland, Support vector machine, ROC Curve, Retrospective Studies, Cross Sectional Studies, and Cross-infection
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Research Interests: Algorithms, Machine Learning, Data Mining, Data Analysis, Mass Spectrometry, and 17 moreProteomics, Support Vector Machines, Biomarker discovery, Feature Selection, Biological Sciences, Stroke, Humans, Female, Male, Support vector machine, Aged, Middle Aged, Adult, Biological markers, Data Consistency, Sensitivity and Specificity, and Gene expression profiling
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Abstract. The selection of an appropriate inducer is crucial for per-forming effective classification. In previous work we presented a system called NOEMON which relied on a mapping between dataset character-istics and inducer performance... more
Abstract. The selection of an appropriate inducer is crucial for per-forming effective classification. In previous work we presented a system called NOEMON which relied on a mapping between dataset character-istics and inducer performance to propose inducers for ...
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Abstract. Meta-learning for model selection, as reported in the sym-bolic machine learning community, can be described as follows. First, it is cast as a purely data-driven predictive task. Second, it typically relies on a mapping of... more
Abstract. Meta-learning for model selection, as reported in the sym-bolic machine learning community, can be described as follows. First, it is cast as a purely data-driven predictive task. Second, it typically relies on a mapping of dataset characteristics to some measure ...
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Abstract. Protein fingerprints are groups of conserved motifs which can be used as diagnostic signatures to identify and characterize collections of protein sequences. These fingerprints are stored in the prints database after... more
Abstract. Protein fingerprints are groups of conserved motifs which can be used as diagnostic signatures to identify and characterize collections of protein sequences. These fingerprints are stored in the prints database after time-consuming annotation by domain experts who ...
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CiteSeerX - Document Details (Isaac Councill, Lee Giles): . Since the mid-1980s, researchers have been pursuing the goal of neurosymbolic integration, ie, the construction of systems capable of both symbolic and neural processing. We... more
CiteSeerX - Document Details (Isaac Councill, Lee Giles): . Since the mid-1980s, researchers have been pursuing the goal of neurosymbolic integration, ie, the construction of systems capable of both symbolic and neural processing. We distinguish two major avenues toward this ...
Distance-based learning over extended relational algebra structures Adam Woznica and Alexandros Kalousis and Melanie Hilario University of Geneva, Computer Science Department Rue General Dufour 24, 1211 Geneva 4, Switzerland {woznica,... more
Distance-based learning over extended relational algebra structures Adam Woznica and Alexandros Kalousis and Melanie Hilario University of Geneva, Computer Science Department Rue General Dufour 24, 1211 Geneva 4, Switzerland {woznica, kalousis, hilario}@ cui. unige. ch ...
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A Data Mining Ontology for Algorithm Selection and Meta-Mining Melanie Hilario, Alexandros Kalousis, Phong Nguyen, Adam Woznica University of Geneva, Department of Computer Science Artificial Intelligence Laboratory CUI-7, route de Drize,... more
A Data Mining Ontology for Algorithm Selection and Meta-Mining Melanie Hilario, Alexandros Kalousis, Phong Nguyen, Adam Woznica University of Geneva, Department of Computer Science Artificial Intelligence Laboratory CUI-7, route de Drize, CH-1227 Carouge, Switzerland { ...
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Mass spectrometry is becoming an important tool in pro-teomics. Mass spectral data are characterized by very high dimensionality and a high level of redundancy. Both issues are quite challenging when one wants to perform knowl-edge... more
Mass spectrometry is becoming an important tool in pro-teomics. Mass spectral data are characterized by very high dimensionality and a high level of redundancy. Both issues are quite challenging when one wants to perform knowl-edge discovery and push existing tools to their limits. ...
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We propose a knowledge-based approach to the task of determining the topology of multilayer perceptrons (MLPs). The idea consists in integrating well-founded and empirically proven configuration techniques into a knowledge-based system. A... more
We propose a knowledge-based approach to the task of determining the topology of multilayer perceptrons (MLPs). The idea consists in integrating well-founded and empirically proven configuration techniques into a knowledge-based system. A preliminary study showed that the use of ...
Abstract. Selecting the most appropriate learning algorithm for a given task has become a crucial research issue since the advent of multi-paradigm data mining tool suites. To address this issue, researchers have tried to extract dataset... more
Abstract. Selecting the most appropriate learning algorithm for a given task has become a crucial research issue since the advent of multi-paradigm data mining tool suites. To address this issue, researchers have tried to extract dataset characteristics which might provide clues ...
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Research Interests: Computer Architecture, Artificial Intelligence, Computer Vision, Pattern Recognition, Image Analysis, and 11 moreImage segmentation, Learning, Document Analysis, Text Analysis, Bayesian methods, Knowledge Based Systems, Feature Extraction, Image recognition, Detectors, Domain Knowledge, and Object Oriented
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Matching Based Kernels for Labeled Graphs Adam Woznica and Alexandros Kalousis and Melanie Hilario University of Geneva, Computer Science Department Rue General Dufour 24, 1211 Geneva 4, Switzerland {woznica, kalousis, hilario}@ cui.... more
Matching Based Kernels for Labeled Graphs Adam Woznica and Alexandros Kalousis and Melanie Hilario University of Geneva, Computer Science Department Rue General Dufour 24, 1211 Geneva 4, Switzerland {woznica, kalousis, hilario}@ cui. unige. ch Abstract. For various ...
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In this paper; the term 'knowledge-based neural network (NN) design ' is used to refer to all efforts at exploiting prior knowledge in neural network configuration and training. A van'ety... more
In this paper; the term 'knowledge-based neural network (NN) design ' is used to refer to all efforts at exploiting prior knowledge in neural network configuration and training. A van'ety of techniques have been proposed for this purpose; SCANDAL provides a workbench for evaluating ...
Research Interests: Machine Learning, Data Mining, Production, Performance, Neural Networks, and 14 moreNeural Network, Learning, Software Agents, Decision Trees, Generalization, IT Strategy, Multi Agent Systems, Training data, Knowledge Based Systems, Prior Knowledge, Domain Knowledge, Knowledge base, Experiments, and Network Topology
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Abstract. Building an effective classifer involves choosing the model class with the appropriate learning bias as well as the right level of com-plexity within that class. These two aspects have rarely been addressed together: typically,... more
Abstract. Building an effective classifer involves choosing the model class with the appropriate learning bias as well as the right level of com-plexity within that class. These two aspects have rarely been addressed together: typically, model class (or algorithm) selection is ...
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Abstract. This paper describes an attempt to improve neural network design through the use of prior knowledge. The idea is to give preference to design techniques which exploit available domain knowledge (either approximate domain... more
Abstract. This paper describes an attempt to improve neural network design through the use of prior knowledge. The idea is to give preference to design techniques which exploit available domain knowledge (either approximate domain theories or partial knowledge about the ...