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Chapter and Conference Paper
Genomic Variant Classifier Tool
The exome or genome based high throughput screening techniques are becoming a definitive criterion in the conventional clinical analysis of the genetic diseases. However, pathogenic classification of an identi...
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Chapter and Conference Paper
Ensemble of Trees for Classifying High-Dimensional Imbalanced Genomic Data
Machine learning for data mining applications in the field of bioinformatics is to extract new knowledge to provide an improved and effective diagnosis process for patients. In this paper, we introduce an adap...
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Chapter and Conference Paper
Knowledge Gradient for Online Reinforcement Learning
The most interesting challenge for a reinforcement learning agent is to learn online in unknown large discrete, or continuous stochastic model. The agent has not only to trade-off between exploration and explo...
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Chapter
Scalarized and Pareto Knowledge Gradient for Multi-objective Multi-armed Bandits
A multi-objective multi-armed bandit (MOMAB) problem is a sequential decision process with stochastic reward vectors. We extend knowledge gradient (KG) policy to the MOMAB problem, and we propose Pareto-KG and...
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Chapter and Conference Paper
Alignment Methods for Folk Tune Classification
This paper studies the performance of alignment methods for folk music classification. An edit distance approach is applied to three datasets with different associated classification tasks (tune family, geogra...
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Chapter and Conference Paper
Schemata Bandits for Binary Encoded Combinatorial Optimisation Problems
We introduce the schemata bandits algorithm to solve binary combinatorial optimisation problems, like the trap functions and NK landscape, where potential solutions are represented as bit strings. Schemata ban...
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Chapter and Conference Paper
Heterogeneous Populations of Learning Agents in the Minority Game
We study how a group of adaptive agents can coordinate when competing for limited resources. A popular game theoretic model for this is the Minority Game. In this article we show that the coordination among le...
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Chapter and Conference Paper
Learning in Minority Games with Multiple Resources
We study learning in Minority Games (MG) with multiple resources. The MG is a repeated conflicting interest game involving a large number of agents. So far, the learning mechanisms studied were rather naive an...
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Article
Open AccessBioLMiner and the BioCreative II.5 challenge
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Chapter and Conference Paper
Named Entity Recognition in Biomedical Literature: A Comparison of Support Vector Machines and Conditional Random Fields
In this paper, we propose two named entity recognition systems for biomedical literature, System1 using support vector machines and System2 using conditional random fields. Through employing several sets of exper...
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Chapter and Conference Paper
Evaluating and Comparing Biomedical Term Identification Systems
In this paper, we propose a term identification system using conditional random fields (CRFs) on two biomedical datasets. Through employing several sets of experiments, we make a comprehensive investigation fo...
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Chapter
Causal Graphical Models with Latent Variables: Learning and Inference
This chapter discusses causal graphical models for discrete variables that can handle latent variables without explicitly modeling them quantitatively. In the uncertainty in artificial intelligence area there exi...
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Chapter and Conference Paper
Causal Graphical Models with Latent Variables: Learning and Inference
Several paradigms exist for modeling causal graphical models for discrete variables that can handle latent variables without explicitly modeling them quantitatively. Applying them to a problem domain consists ...
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Chapter and Conference Paper
Context-Sensitive Kernel Functions: A Distance Function Viewpoint
This paper extends the idea of weighted distance functions to kernels and support vector machines. Here, we focus on applications that rely on sliding a window over a sequence of string data. For this type of ...
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Chapter and Conference Paper
Learning Causal Bayesian Networks from Observations and Experiments: A Decision Theoretic Approach
We discuss a decision theoretic approach to learn causal Bayesian networks from observational data and experiments. We use the information of observational data to learn a completed partially directed acyclic ...
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Chapter
An Evolutionary Game Theoretic Perspective on Learning in Mult-Agent Systems
In this paper we revise Reinforcement Learning and adaptiveness in Multi-Agent Systems from an Evolutionary Game Theoretic perspective. More precisely we show there is a triangular relation between the fields ...
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Chapter and Conference Paper
Appropriate Kernel Functions for Support Vector Machine Learning with Sequences of Symbolic Data
In classification problems, machine learning algorithms often make use of the assumption that (dis)similar inputs lead to (dis)similar outputs. In this case, two questions naturally arise: what does it mean fo...
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Article
An Evolutionary Game Theoretic Perspective on Learning in Multi-Agent Systems
In this paper we revise Reinforcement Learning and adaptiveness in Multi-Agent Systems from an Evolutionary Game Theoretic perspective. More precisely we show there is a triangular relation between the fields ...
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Chapter and Conference Paper
Extended Replicator Dynamics as a Key to Reinforcement Learning in Multi-agent Systems
Modeling learning agents in the context of Multi-agent Systems requires an adequate understanding of their dynamic behaviour. Evolutionary Game Theory provides a dynamics which describes how strategies evolve ...
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Chapter and Conference Paper
Reinforcement Learning in Large State Spaces
Large state spaces and incomplete information are two problems that stand out in learning in multi-agent systems. In this paper we tackle them both by using a combination of decision trees and Bayesian network...
