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Mining mass-spectra for diagnosis and biomarker - (CUI)
Mining mass-spectra for diagnosis and biomarker - (CUI)

... Peak detection was done within the Ciphergen ProteinChip Software. We used the software in order to determine a list of peaks for each spectrum. This was done for a single spectrum each time without taking into account the remaining spectra; the final outcome was a list of peaks for each spectrum. T ...
Querying and Mining Data Streams
Querying and Mining Data Streams

An investigation on local wrinkle-based extractor of age estimation
An investigation on local wrinkle-based extractor of age estimation

... extractor for age estimation. AAM decouples and models two parts of an object: shape and texture. Adopting the AAM allowed exploration of combined shape and intensity model to represent face images (Lanitis et al., 2004). Face images were represented by means of lower dimension model parameters givi ...
Short and Sparse Text Topic Modeling via Self-Aggregation
Short and Sparse Text Topic Modeling via Self-Aggregation

DECODE: a new method for discovering clusters of different
DECODE: a new method for discovering clusters of different

... Density-based cluster methods are characterized by aggregating mechanisms based on density (Han et al. 2001). It is believed that density-based cluster methods have the potential to reveal the structure of a spatial data set in which different point processes overlap. Ester et al. (1996) and Sander ...
Mining and Using Sets of Patterns through Compression
Mining and Using Sets of Patterns through Compression

A Framework for Measuring Changes in Data Characteristics
A Framework for Measuring Changes in Data Characteristics

A Computational Intelligence Approach to Modelling Interstate Conflict
A Computational Intelligence Approach to Modelling Interstate Conflict

... I wish to thank my mother and father for all the support they have given me throughout my studies. Their input has made it possible for me to push towards attaining higher levels in my education. I would like to thank my siblings especially my eldest brother for his advice and encouragement. I would ...
ES23861870
ES23861870

Answers to Exercises
Answers to Exercises

... Let's pick sore throat as the top-level node. The only possibilities are yes and no. Instances one, three four, eight, and ten follow the yes path. The no path shows instances 2,5,6,7 & 9. The path for sore throat = yes has representatives from all three classes as does sore throat = no. Next we fol ...
Selectivity and sparseness in the responses of striate complex cells
Selectivity and sparseness in the responses of striate complex cells

A K-means-like Algorithm for K-medoids Clustering and Its
A K-means-like Algorithm for K-medoids Clustering and Its

(SDSS - FORTH)hot! - SensorART. All Rights Reserved.
(SDSS - FORTH)hot! - SensorART. All Rights Reserved.

... Evaluation was performed (i) the 10-fold stratified cross validation method and (ii) the initial dataset (before the resampling) and the respective ...
Customer churn prediction for an insurance company
Customer churn prediction for an insurance company

Ensemble Methods
Ensemble Methods

Generative Inferences Based on Learned Relations
Generative Inferences Based on Learned Relations

... derived independently of the model. A number of alternative feature representations have been used as inputs to BART, of which the richest and most complex feature representations were derived by applying the topic model (Griffiths, Steyvers, & Tenenbaum, 2007) to the English Wikipedia corpus. The o ...
On the Power of Ensemble: Supervised and Unsupervised Methods
On the Power of Ensemble: Supervised and Unsupervised Methods

Data Mining - PhD in Information Engineering
Data Mining - PhD in Information Engineering

Decision Trees for Uncertain Data
Decision Trees for Uncertain Data

... ask a question like, “How many hours of TV do you watch each week?” A typical respondent would not reply with an exact precise answer. Rather, a range (e.g., “6–8 hours”) is usually replied, possibly because the respondent is not so sure about the answer himself. In this example, the survey can rest ...
Naive Bayesian Classification Approach in Healthcare Applications
Naive Bayesian Classification Approach in Healthcare Applications

... mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). Bayesian approaches are a fundamentally important DM technique. Given the probability distribution, Bayes classifier can provably achieve the optimal result. Bayesian method is based on the probability theory. Ba ...
Generative Adversarial Structured Networks
Generative Adversarial Structured Networks

... we enforce structure more explicitly by leveraging collective inference. For instance, it is often the case that neighboring pixels in an image have similar values. This type of reasoning is often encoded by a scoring function that encourages adjacent pixel variables to have the same value. By joint ...
Constructing Probability Boxes and Dempster
Constructing Probability Boxes and Dempster

... assumption about the distribution shape of the underlying random variable and the associated parameters of the distribution, (2) decomposing the quantity in question in terms of a model involving other, more easily estimated quantities, (3) using robust Bayes methods to update a class of possible pr ...
A neural implementation of Bayesian inference based on predictive
A neural implementation of Bayesian inference based on predictive

... values; 1 and 2 are parameters; and and ⊗ indicate element-wise division and multiplication respectively. For all the experiments described in this paper 1 and 2 were given the values 1 × 10−6 and 1 × 10−4 respectively. Parameter 1 prevents prediction neurons becoming permanently non-responsi ...
NBER WORKING PAPER SERiES THE DISTRIBUTION OF EXCHANGE RATES IN THE EMS
NBER WORKING PAPER SERiES THE DISTRIBUTION OF EXCHANGE RATES IN THE EMS

A Bayesian Committee Machine
A Bayesian Committee Machine

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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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