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... The key parameters of SVR, i.e. σ (the width of RBF kernel), C (penalty factor) and ε (insensitive loss function) have a great influence on the accuracy of SVM regression. They are given by experience or test without better ways before. To avoid the blindness and low efficiency of selecting paramete ...
A Property & Casualty Insurance Predictive Modeling Process in SAS
A Property & Casualty Insurance Predictive Modeling Process in SAS

... to conclusions about the item's target value. A decision tree divides data into groups by applying a series of simple rules. Each rule assigns an observation to a group based on the value of one input. One rule is applied after another, resulting in a hierarchy of groups. The hierarchy is called a t ...
Different parameters - same prediction: An analysis of learning curves
Different parameters - same prediction: An analysis of learning curves

... In a first study, we analyzed the parameter fit of different regression models and evaluated their performance in prediction of new items. Furthermore, we compared prediction accuracy of regression models to that of traditional BKT. We used all the samples until the children mastered a skill and pre ...
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Office hrs

... Partial Least Squares Regression was invented by the Swedish statistician Herman Wold for econometrics applications. In the meanwhile PLS has become a popular and powerful tool in chemometrics, but has been partially ignored in mainstream statistics. Svante Wold (the son of Herman Wold) popularized ...
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Granular Box Regression Methods for Outlier Detection
Granular Box Regression Methods for Outlier Detection

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View PDF - CiteSeerX
View PDF - CiteSeerX

... models for the same algorithms the nonparametric Wilcoxon signed-rank tests were carried out for three measures: MSE, MAE and MAPE. The results are shown in Table 6, where a triple in each cell, eg , reflects outcome for each pair of models created by KEEL-RM, KEEL-WEKA, and RM-WEKA respectivel ...
Fraud Detection Model
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Ancestry Assessment Using Random Forest Modeling

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COMPARITIVE ANALYSIS OF FUZZY DECISION TREE AND

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Powerpoint Slides Discussing our Constructive Induction / Decision

... NLREG, like most conventional regression analysis packages, is only capable of finding the numeric coefficients for a function whose form (i.e. linear, quadratic, or polynomial) has been prespecified by the user. A poor choice, made by the user, of the functions form will in most cases lead to a ver ...
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Machine Learning: Generative and Discriminative Models

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... nearest neighbor to the query and different K yields different conditional class probabilities. If K is very small, the local estimate tends to be very poor owing to the data sparseness and the noisy, ambiguous or mislabeled points. In order to further smooth the estimate, we can increase K and take ...
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STATISTICA Enterprise 8: Marketing and Sales

... (categorical) outcome variable (dependent variable); for multinomial (multiple-category) outcome variables, lift charts can be computed for each category. Specifically, the chart summarizes the utility that one may expect by using the respective predictive models compared to using baseline informati ...
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View - Association for Computational Linguistics

... about the weekend ahead, or comment about their favorite weekly TV show during its air time. Given this, text frequencies will display periodic patterns. This applies to other text related quantities like cooccurrence values or topic distributions over time, as well as applications outside NLP like ...
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performance comparison of time series data using predictive data

... Typically, PEs are structured into layers and the output values of PEs in one layer serve as input values for PEs in the next layer. Each connection has a weight associated with it. In most cases, a Processing Element calculates a weighted sum of incoming values (the sum the outputs of the PEs conne ...
pptx - University of Pittsburgh
pptx - University of Pittsburgh

... • Notice that it relies on an inner product between the test point x and the support vectors xi • (Solving the optimization problem also involves computing the inner products xi · xj between all pairs of training points) C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Data ...
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Applying Data Mining Techniques in the Development of a

... for classification in the questionnaire. Then we used neural network models (see below for more details), decision tree algorithms, and other classification algorithms to construct the best model to classify GERD, based on our data. An important issue in data mining is the generalization of the clas ...
Data Mining Tutorial
Data Mining Tutorial

... • Sunday football highlights always look good! • If he shoots enough times, even a 95% free throw shooter will miss. • Tried 49 splits, each has 5% chance of declaring significance even if there’s no relationship. ...
Data Mining Tutorial
Data Mining Tutorial

... • Sunday football highlights always look good! • If he shoots enough times, even a 95% free throw shooter will miss. • Tried 49 splits, each has 5% chance of declaring significance even if there’s no relationship. ...
older_Data Mining Tutorial
older_Data Mining Tutorial

... • Sunday football highlights always look good! • If he shoots enough times, even a 95% free throw shooter will miss. • Tried 49 splits, each has 5% chance of declaring significance even if there’s no relationship. ...
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Multinomial logistic regression

In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes. That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables (which may be real-valued, binary-valued, categorical-valued, etc.).Multinomial logistic regression is known by a variety of other names, including polytomous LR, multiclass LR, softmax regression, multinomial logit, maximum entropy (MaxEnt) classifier, conditional maximum entropy model.
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