classification error
Approximation of unbiased convex classification error rate estimator
... Classification error rate estimation is critical for hyperparameter selection [3, 5], feature selection [10] and combining classifiers ...of error estimation methods have been proposed [6, 7, 8, 11, ...
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Combined classification error rate estimator for the Fisher linear classifier
... tion error rate estimator designed specially for the Fisher linear ...combined classification error rate estimator by using fixed weight that is calculated from asymptotic approximations of expected ...
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Principal Component Analysis for Supervised Learning: a minimum classification error approach
... for classification problems, since projections of maximal variance may mix points from different classes [Bishop 2006] within the same ...for classification than another ...Bayes error rate for ...
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An experimental evaluation of the incidence of fitness-function/search-algorithm combinations on the classification performance of myoelectric control systems with iPCA tuning
... lowing: classification error, correlation factor, and for search algorithms: SFS, SFFS, PSO and ...on classification perfor- mance when reduced iPCA matrices computed with classification ...
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A Plug-in Approach to Neyman-Pearson Classification
... NP classification leads to problems related to density level set estimation (see Rigollet and Vert 2009 and reference therein), where the task is to estimate { x : p(x) > λ } , for some level λ > ...binary ...
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A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search
... the classification error rate of prostate needle biopsies for the four groups mentioned above while removing as many features as pos- sible from the original feature ...problem. Classification ...
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Data Mining Decision Trees in Economy
... the classification of the different DT, the accuracy of classification on the test data, which are completely unknown at the DT training, is represented along with the performance number of attributes used ...
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Title: Biometric Authentication of an Individual Using Multilayer Perceptron and Support Vector Machine
... for classification. SVM simultaneously minimize the classification error and maximize the geometric margin hence called maximum margin ...generalization error of the ...
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Small-Sample Error Estimation for Bagged Classification Rules
... of error estimation, Breiman proposed a general method, which he called “out- of-bag”, for estimating statistics of bagged classifiers [21], and, subsequently, other authors applied it to the estimation of the ...
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A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation
... or classification error-rate and EP methods to minimise negative log-likelihood, due to the form of their respective energies (the variational free-energy includes the average training MSE in the regression ...
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Learning and Using Taxonomies for Visual and Olfactory Classification
... data classification error consistently increased as sensors were added to the array, with the errors becoming increasingly close to the level expected for random ...Control error was 17% less than ...
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Learning long-term filter banks for audio source separation and audio scene classification
... reconstruction error in audio source separation task is reduced by relatively ...the classification error in audio scene classification task is reduced by relatively ...in ...
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The Positional Effect in Soft Classification Accuracy Assessment
... Figure 2 demonstrates the impact of positional errors on overall accuracy of eight study sites at a spatial resolution of 150 meters. Within the group using the 8-class scheme, the rate of growth depends on the spatial ...
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Asymptotic Performance of the Location and Logistic Classification Rules for Multivariate Binary Variables
... statistical classification technique for separating distinct sets of objects and allocating a new object to a previously defined ...as classification, pattern recognition and character recognition, ...
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Automatic error classification with multiple error labels
... matic error classification approach by suggesting multiple error categories for each ...each error category we are able to as- sign a (fractional) count which intuitively can be interpreted as ...
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Human Activity Recognition through Accelerometer Sensor Using Data Mining
... The classification parameters used in this study are mean and standard deviation of each ...different classification algorithms like J48, Naïve bayes, Random forest, random tree, multilayer perceptron were ...
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Error Free Training via Information Structuring in the Classification Problem
... The present paper solves the training problem that comprises the initial phases of the classification problem using the data matrix invariant method. The method is reduced to an approximate “slicing” of the ...
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Grammatical Error Detection and Correction using a Single Maximum Entropy Model
... One traditional strategy is designing a system combined of a set of sub-models, where each sub- model is specialized for a specific subtask, for ex- ample, correcting one type of errors. This strat- egy is ...
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DEVELOPMENT AND APPLICATION OF A STAGE GATE PROCESS TO REDUCE THE UNERLYING RISKS OF IT SERVICE PROJECTS
... Haralick et al. [11] introduced a second order statistical method “grey level co-occurrence matrix (GLCM)” for texture analysis. The GLCM has become one of the standard and benchmark methods for texture analysis. The ...
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Correct Classification Rates in Multi Category Discriminant Analysis of Spatial Gaussian Data
... of error rate estimation in multi-category linear discriminant analysis for mul- tivariate homoscedastic Gaussian data was performed by Hirst ...multiclass classification problem for correlated Gaussian ...
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