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Clustering Formulation using Constraint Optimization
Clustering Formulation using Constraint Optimization

discrete variational autoencoders
discrete variational autoencoders

Bayesian Network Classifiers
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 ...
A Game-theoretic Machine Learning Approach for Revenue
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 ...
Dirichlet Processes: Tutorial and Practical Course (updated)
Dirichlet Processes: Tutorial and Practical Course (updated)

Statistical Learning Theory
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 ...
A Class Imbalance Learning Approach to Fraud
A Class Imbalance Learning Approach to Fraud

Quantitative Evaluation of Approximate Frequent Pattern Mining
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 ...
Pricing Excess of Loss Treaty with Loss Sensitive Features: An
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 ...
Harold Jeffreys`s Theory of Probability Revisited
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 ...
Efficient Discovery of Error-Tolerant Frequent Itemsets in High
Efficient Discovery of Error-Tolerant Frequent Itemsets in High

A scored AUC Metric for Classifier Evaluation and Selection
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 ...
Following non-stationary distributions by controlling the
Following non-stationary distributions by controlling the

Multivariate Discretization for Set Mining
Multivariate Discretization for Set Mining

Farthest Neighbor Approach for Finding Initial Centroids in K
Farthest Neighbor Approach for Finding Initial Centroids in K

An Introduction to Variational Methods for Graphical Models
An Introduction to Variational Methods for Graphical Models

Institutionen f¨ or datavetenskap An Evaluation of Clustering and Classification Algorithms in
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, ...
A survey on multi-output regression
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. ...
Data Mining Techniques for Mortality at Advanced Age
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 ...
Hardness-Aware Restart Policies
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 ...
Data Mining Tutorial
Data Mining Tutorial

From Dependence to Causation
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 ...
Temporal data - ResearchGate
Temporal data - ResearchGate

Data Summarization with Social Contexts - Infoscience
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 ...
Toward Privacy in Public Databases
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 ...
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Mixture model

In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs. Formally a mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the overall population. However, while problems associated with ""mixture distributions"" relate to deriving the properties of the overall population from those of the sub-populations, ""mixture models"" are used to make statistical inferences about the properties of the sub-populations given only observations on the pooled population, without sub-population identity information.Some ways of implementing mixture models involve steps that attribute postulated sub-population-identities to individual observations (or weights towards such sub-populations), in which case these can be regarded as types of unsupervised learning or clustering procedures. However not all inference procedures involve such steps.Mixture models should not be confused with models for compositional data, i.e., data whose components are constrained to sum to a constant value (1, 100%, etc.). However, compositional models can be thought of as mixture models, where members of the population are sampled at random. Conversely, mixture models can be thought of as compositional models, where the total size of the population has been normalized to 1.
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