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Intro; Preface; Organization; Contents; Early Detection of Sepsis Induced Deterioration Using Machine Learning; 1 Introduction; 2 Dataset; 3 Feature Extraction Methods; 3.1 Histograms of Derivatives; 3.2 of Histograms of Derivatives; 3.3 Wavelet Transform and Autoregressive Modelling; 4 Machine Learning Methods; 4.1 Linear Support Vector Machine; 4.2 Random Forest; 4.3 Gradient Boosting Machine; 4.4 Weighted k-Nearest Neighbors; 4.5 Multi-Layer Perceptron; 4.6 Naïve Bayes Classifier; 4.7 Logistic Regression; 5 Experiments and Results; 6 Conclusion and Future Work; References
Deriving Formulas for Integer Sequences Using Inductive Programming1 Introduction; 2 Problem Statement; 3 Approach; 3.1 Windows; 3.2 Linear Combinations; 3.3 Feature Vectors; 3.4 Algorithm; 3.5 Limitations; 4 Implementation; 5 Experiments; 5.1 Sequences in OEIS Categories; 5.2 Input Length; 5.3 Feature Vectors; 6 Comparison with Other Methods; 6.1 MagicHaskeller; 6.2 IGOR; 6.3 Neural Networks; 7 Further Work; 8 Conclusion; References; All or In-cloud: How the Identification of Six Types of Anomalies Is Affected by the Discretization Method; Abstract; 1 Introduction; 2 Theoretical Foundations
2.1 Typology of Anomalies2.2 Discretization; 2.3 SECODA; 3 Empirical Experiments; 3.1 Research Design and Datasets; 3.2 Results and Discussion; 4 Conclusion; References; Topic Modeling for Exploring Cancer-Related Coverage in Journalistic Texts; Abstract; 1 Introduction; 2 Method; 2.1 Data Collection; 2.2 Data Pre-processing; 2.3 Latent Dirichlet Allocation; 3 Results; 4 Discussion and Conclusion; 4.1 Discussion of Results; 4.2 General Discussion; 4.3 Conclusion; References; Model Selection for Multi-directional Ensemble of Regression and Classification Trees; 1 Introduction; 2 Related Work
3 Selection Strategies for MERCS4 Experiments; 5 Discussion; References; Finding Dissimilar Explanations in Bayesian Networks: Complexity Results; 1 Introduction; 2 Preliminaries; 3 Main Results; 3.1 On Membership in NPPP; 4 Conclusion; References; Beyond Local Nash Equilibria for Adversarial Networks; 1 Introduction; 2 Background; 3 GANGs; 4 Solving GANGs; 5 Experiments; 6 Discussion; 7 Related Work; 8 Conclusions; References; Deep Multi-agent Reinforcement Learning in a Homogeneous Open Population; 1 Introduction; 2 Background; 3 Problem Setting; 3.1 Environment; 3.2 State and Action Space
3.3 Parameters4 Methods; 4.1 From Single to Multi-agent Learning; 4.2 Single to Multi-agent Knowledge Transfer; 5 Experiments; 5.1 Single-Agent; 5.2 Multi-agent from Scratch; 5.3 Multi-agent with Single-agent Initialization; 6 Related Work; 7 Conclusions; References; Computing and Predicting Winning Hands in the Trick-Taking Game of Klaverjas; 1 Introduction; 2 Related Work; 3 Preliminaries; 4 Exact Approach; 4.1 Combinatorics; 4.2 Solving Approach; 4.3 Equivalence Classes; 5 Machine Learning Approach; 6 Experiments; 6.1 Exact Approach Results; 6.2 Machine Learning Results
Deriving Formulas for Integer Sequences Using Inductive Programming1 Introduction; 2 Problem Statement; 3 Approach; 3.1 Windows; 3.2 Linear Combinations; 3.3 Feature Vectors; 3.4 Algorithm; 3.5 Limitations; 4 Implementation; 5 Experiments; 5.1 Sequences in OEIS Categories; 5.2 Input Length; 5.3 Feature Vectors; 6 Comparison with Other Methods; 6.1 MagicHaskeller; 6.2 IGOR; 6.3 Neural Networks; 7 Further Work; 8 Conclusion; References; All or In-cloud: How the Identification of Six Types of Anomalies Is Affected by the Discretization Method; Abstract; 1 Introduction; 2 Theoretical Foundations
2.1 Typology of Anomalies2.2 Discretization; 2.3 SECODA; 3 Empirical Experiments; 3.1 Research Design and Datasets; 3.2 Results and Discussion; 4 Conclusion; References; Topic Modeling for Exploring Cancer-Related Coverage in Journalistic Texts; Abstract; 1 Introduction; 2 Method; 2.1 Data Collection; 2.2 Data Pre-processing; 2.3 Latent Dirichlet Allocation; 3 Results; 4 Discussion and Conclusion; 4.1 Discussion of Results; 4.2 General Discussion; 4.3 Conclusion; References; Model Selection for Multi-directional Ensemble of Regression and Classification Trees; 1 Introduction; 2 Related Work
3 Selection Strategies for MERCS4 Experiments; 5 Discussion; References; Finding Dissimilar Explanations in Bayesian Networks: Complexity Results; 1 Introduction; 2 Preliminaries; 3 Main Results; 3.1 On Membership in NPPP; 4 Conclusion; References; Beyond Local Nash Equilibria for Adversarial Networks; 1 Introduction; 2 Background; 3 GANGs; 4 Solving GANGs; 5 Experiments; 6 Discussion; 7 Related Work; 8 Conclusions; References; Deep Multi-agent Reinforcement Learning in a Homogeneous Open Population; 1 Introduction; 2 Background; 3 Problem Setting; 3.1 Environment; 3.2 State and Action Space
3.3 Parameters4 Methods; 4.1 From Single to Multi-agent Learning; 4.2 Single to Multi-agent Knowledge Transfer; 5 Experiments; 5.1 Single-Agent; 5.2 Multi-agent from Scratch; 5.3 Multi-agent with Single-agent Initialization; 6 Related Work; 7 Conclusions; References; Computing and Predicting Winning Hands in the Trick-Taking Game of Klaverjas; 1 Introduction; 2 Related Work; 3 Preliminaries; 4 Exact Approach; 4.1 Combinatorics; 4.2 Solving Approach; 4.3 Equivalence Classes; 5 Machine Learning Approach; 6 Experiments; 6.1 Exact Approach Results; 6.2 Machine Learning Results