Bayesian Network Classifiers
... In order to tackle this problem effectively, we need an appropriate language and efficient machinery to represent and manipulate independence assertions. Both are provided by Bayesian networks (Pearl, 1988). These networks are directed acyclic graphs that allow efficient and effective representation ...
... In order to tackle this problem effectively, we need an appropriate language and efficient machinery to represent and manipulate independence assertions. Both are provided by Bayesian networks (Pearl, 1988). These networks are directed acyclic graphs that allow efficient and effective representation ...
A Game-theoretic Machine Learning Approach for Revenue
... symmetric Nash equilibria is maximized, and in [Garg and Narahari, 2009; Garg et al., 2007] the Bayesian optimal auction mechanism design is investigated with the value distribution of the bidders as public knowledge. In these works, some ideal assumptions have been employed. For instance, one usual ...
... symmetric Nash equilibria is maximized, and in [Garg and Narahari, 2009; Garg et al., 2007] the Bayesian optimal auction mechanism design is investigated with the value distribution of the bidders as public knowledge. In these works, some ideal assumptions have been employed. For instance, one usual ...
Statistical Learning Theory
... there exists a joint probability distribution P on X × Y, and the training examples (Xi , Yi ) are sampled independently from this distribution P . This type of sampling is often denoted as iid sampling (independent and identically distributed). There are a few important facts to note here. 1. No as ...
... there exists a joint probability distribution P on X × Y, and the training examples (Xi , Yi ) are sampled independently from this distribution P . This type of sampling is often denoted as iid sampling (independent and identically distributed). There are a few important facts to note here. 1. No as ...
Quantitative Evaluation of Approximate Frequent Pattern Mining
... To handle this situation, approximate frequent itemsets (AFI) [5] were proposed. AFIs enforce constraints on the number of missing items in both rows and columns. One of the advantages of AFIs over weak/strong ETIs is that there is a limited version of an anti-monotone property that helps prune the ...
... To handle this situation, approximate frequent itemsets (AFI) [5] were proposed. AFIs enforce constraints on the number of missing items in both rows and columns. One of the advantages of AFIs over weak/strong ETIs is that there is a limited version of an anti-monotone property that helps prune the ...
Pricing Excess of Loss Treaty with Loss Sensitive Features: An
... at the time of pricing we cannot estimate their value. However based on the aggregate losses we can estimate their expected value. It is therefore very important to be able to estimate an appropriate aggregate loss distribution function that can be used to estimate the expected premium income and ex ...
... at the time of pricing we cannot estimate their value. However based on the aggregate losses we can estimate their expected value. It is therefore very important to be able to estimate an appropriate aggregate loss distribution function that can be used to estimate the expected premium income and ex ...
Harold Jeffreys`s Theory of Probability Revisited
... In contrast, the frequentist theories of Neyman or of Fisher require the choice of ad hoc procedures, whose (good or bad) properties they later analyze. But this may be a far-fetched interpretation of this rule at this stage even though the comment will appear more clearly later. 2. The theory must ...
... In contrast, the frequentist theories of Neyman or of Fisher require the choice of ad hoc procedures, whose (good or bad) properties they later analyze. But this may be a far-fetched interpretation of this rule at this stage even though the comment will appear more clearly later. 2. The theory must ...
A scored AUC Metric for Classifier Evaluation and Selection
... eight for training, one for validation, and one for testing. We first trained five models, (naive Bayes, logistic, decision tree, kstar, and voting feature interval [2]) on the training set, selected the model with maximum values of sAUC or AUC on the validation set, and finally tested the selected ...
... eight for training, one for validation, and one for testing. We first trained five models, (naive Bayes, logistic, decision tree, kstar, and voting feature interval [2]) on the training set, selected the model with maximum values of sAUC or AUC on the validation set, and finally tested the selected ...
Institutionen f¨ or datavetenskap An Evaluation of Clustering and Classification Algorithms in
... Examples of partitioning clustering algorithms are: • k-means clustering partitions the input data set with n spatial objects into k clusters, each cluster represented by a mean spatial point. An arbitrary point p belongs to cluster C represented by mean m ∈ M if and only if d(p, m) = minm0 ∈M d(p, ...
... Examples of partitioning clustering algorithms are: • k-means clustering partitions the input data set with n spatial objects into k clusters, each cluster represented by a mean spatial point. An arbitrary point p belongs to cluster C represented by mean m ∈ M if and only if d(p, m) = minm0 ∈M d(p, ...
A survey on multi-output regression
... Intelligence Group, Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain Conflict of interest: The authors have declared no conflicts of interest for this article. ...
... Intelligence Group, Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain Conflict of interest: The authors have declared no conflicts of interest for this article. ...
Data Mining Techniques for Mortality at Advanced Age
... When you classify or predict observations, you classify values of nominal or binary targets. For interval targets, you can predict outcomes. Trees produce a set of rules that can be used to generate predictions for a new data set. These rules can also be used to detect interactions among variables a ...
... When you classify or predict observations, you classify values of nominal or binary targets. For interval targets, you can predict outcomes. Trees produce a set of rules that can be used to generate predictions for a new data set. These rules can also be used to detect interactions among variables a ...
Hardness-Aware Restart Policies
... The run time of backtracking heuristic search algorithms is notoriously unpredictable. Gomes et al. [7] demonstrated the effectiveness of randomized restarts on a variety of problems in scheduling, theorem-proving, and planning. In this approach, randomness is added to the branching heuristic of a s ...
... The run time of backtracking heuristic search algorithms is notoriously unpredictable. Gomes et al. [7] demonstrated the effectiveness of randomized restarts on a variety of problems in scheduling, theorem-proving, and planning. In this approach, randomness is added to the branching heuristic of a s ...
From Dependence to Causation
... understanding about how these systems behave under changing, unseen environments. In turn, knowledge about these causal dynamics allows to answer “what if” questions, describing the potential responses of the system under hypothetical manipulations and interventions. Thus, understanding cause and ef ...
... understanding about how these systems behave under changing, unseen environments. In turn, knowledge about these causal dynamics allows to answer “what if” questions, describing the potential responses of the system under hypothetical manipulations and interventions. Thus, understanding cause and ef ...
Data Summarization with Social Contexts - Infoscience
... weights as a K-parameter hidden random variable (i.e., follows Dirilecht distribution) rather than a large set of individual parameters that are linked to each dataset. In this way, the parameter space of LDA model is O(K +Kd) which does not increase linearly in the size of dataset. Therefore, LDA d ...
... weights as a K-parameter hidden random variable (i.e., follows Dirilecht distribution) rather than a large set of individual parameters that are linked to each dataset. In this way, the parameter space of LDA model is O(K +Kd) which does not increase linearly in the size of dataset. Therefore, LDA d ...
Toward Privacy in Public Databases
... We briefly highlight some techniques from the literature. Many additional references appear in the full paper (see the title page of this paper for the URL). Suppression, Aggregation, and Perturbation of Contingency Tables. Much of the statistics literature is concerned with identifying and protecti ...
... We briefly highlight some techniques from the literature. Many additional references appear in the full paper (see the title page of this paper for the URL). Suppression, Aggregation, and Perturbation of Contingency Tables. Much of the statistics literature is concerned with identifying and protecti ...