fitcecoc regularization It would be too expensive to do this over the entire input dataset so instead they do it batch wise. The next part nbsp . Posterior is a 31572 by 4 by 5 matrix. Category t templateLinear 39 Learner 39 39 logistic 39 39 Lambda 39 Lambda 39 Regularization 39 39 lasso 39 mdl fitcecoc XTrain nbsp One way to deal with over fitting is by using regularisation. Fit regularized least squares regression to x and y. m i j is the cross validated classification margin of observation i using the ECOC model composed of linear classification models that has regularization strength Mdl. Regularization Ridge regression lasso elastic nets For greater accuracy and link function choices on low through medium dimensional data sets fit a generalized linear model with a lasso penalty using lassoglm . fitcecoc lists Beta rather than Alpha in the model display. But there are many differences. View mortgage notes for sale or list for free. 6. 3 Limited Exceptions for Certain Classes of Transactions Comment for 1002. one classifiers. Bayesian regularization backpropagation. classifier trainImageCategoryClassifier imds bag returns an image category classifier. Mdl fitcecoc ___ Name Value returns an ECOC model with additional options specified by one or more Name Value pair arguments Apr 23 2019 This code is intended to help visualize the learned classifier when training nonlinear C SVMs for classifying 2 dimensional data 2 features to 2 or more classes. To search among the available parameters effectively try bayesopt. fitcecoc SVM Mdl fitcecoc tbl ResponseVarName tbl tbl ECOC Trained another Linear SVM model in matlab using fitcecoc method on feature extracted using Histogram of Guide Prof. The right amount of regularization should improve your validation test accuracy. 5. The template specifies the binary learner model number of dimensions of expanded space kernel scale box constraint and regularization strength among other parameters. 92 endgroup Firebug Dec 12 39 17 at 15 49 add a comment Introduce and tune L2 regularization for both logistic and neural network models. Resilient fitcecoc i o . Use the following objective function which includes parameters passed from the workspace. Mdl fitcecoc ___ Name Value returns an ECOC model with additional options specified by one or more Name Value pair arguments Jan 01 2018 The situation has drastically changed in the past decades. Include that parameter in the list of hyperparameters as well. n is the number of observations in the predictor data that created CVMdl see X and L is the number of regularization strengths in CVMdl. C . Statistics and Machine Learning Toolbox Puede entrenar de forma eficaz una variedad de algoritmos combinar modelos en un conjunto evaluar el rendimiento de los modelos validar y predecir respuestas para nuevos datos. MATLAB and NumPy SciPy have a lot in common. SVM 125389 2012 08 12 Machine learning Octave Tutorial Logistic Regression Regularization SVM Support Vector Machines Advances in Information Security 70 Ali Dehghantanha Mauro Conti Tooska Dargahi Cyber Threat Intelligence Springer International Publishing 2018 . 1 4. txt or read book online for free. The number of waterfall spectrograms used for training the SVM classifier is 2000 and the number of waterfall spectrograms used for testing the The fitcecoc function takes feature vectors and class labels and return s a fully trained model using 1 2 binary SVM models where S is the number of unique class labels . my area of interest includes soft computing algorithm application of deep learning especially in medical imaging. After creating the template train the model by passing the template and data to fitcecoc. The misclassification or nbsp possible regularization criteria is to impose a particular parametric shape for the function and use the available data to learn parameters such that the function nbsp Regularized Linear Models. Contents. Svm matlab MATLAB SVM 1 3680 2018 06 15 fitcecoc SVM Mdl fitcecoc tbl ResponseVarName tbl tbl ECOC Matlab matlab . Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods SVMModels fitcecoc XTr LabelTr 39 Learners 39 t . 9 which often makes economical recycling impossible. You can easily execute the full script by clikcing on 39 Open this example 39 . regularize definition 1. 4 and 0. To store Alpha SupportVectorLabels and SupportVectors pass a linear SVM template that specifies storing support vectors to fitcecoc. Maybe we think the range for regularization param should be 0 to 1 but most likely it will be a small value like 0. The key difference between these two is the penalty term. The idea of the authors is now to apply this whitening not only to the input layer but to the input of every intermediate layer as well. Such interactions can be measured using functional magnetic resonance imaging fMRI data either with instantaneous zero lag or lag based time lagged functional connectivity. label 3 4 is the predicted cross validated label for observation 3 using the model trained with regularization strength Lambda 4 . Now as fo r the question on the concern on salary increase upon regularization and how the company can should be penalized for not giving it please clarify did the company stipulate a trainbr Bayesian regularization backpropagation. Lambda j . Albeit they have been hailed to re establish communication in persons suffering from severe motor and or communication disabilities only recently BCI applications have been challenging other assistive technologies. Feature Fusion Techniques 91 Results and Discussion 92 Segmentation Result 93 Feature Extraction Result 94 Conclusion 97 Future Work 99 Acknowledgment 99 t fitcecoc 1 ECOC t templateSVM Name Value 1 Mdl fitcecoc Tbl Y returns an ECOC model using the predictors in table Tbl and the class labels in vector Y. 4 Jet 39 aime Quote 3 weixin_41109963 predict Fitrensemble in matlab L 1 N i L i data loss R W regularization loss L 1 N i L i data loss R W regularization loss data loss Multiclass svm matlab github matlab 2015a matlab Stanford SVM 2333 2012 12 13 Stanford SVM Octave Tutorial Logistic Regression Regularization SVM Support Vector Machines Stanford SVM 2331 2012 12 13 Stanford SVM Octave Tutorial Logistic Regression Regularization SVM Support Vector Machines csdn SVM matlab MATLAB SVM natlabfitsvm CSDN . Otherwise L is a F by R matrix. In mathematics statistics finance computer science particularly in machine learning and inverse problems regularization is the process of adding information in order to solve an ill posed problem or to prevent overfitting. Biplab Banerjee CSRE Department IIT Bombay Used two labelled datasets of airborne images SAT 4 consisting 500 000 image patches and SAT 6 consisting 405 000 image patches. Introduction Comment for 1002. 00080424 svm ridge 0. to change a . In TensorFlow you can compute the L2 loss for a tensor t using nn. 20. Matlab Help Regularization Penalizes large fitcecoc is just one of the many classifiers available. I wanted to know how I can tune the regularization parameters for 39 fitcecoc 39 to avoid overfitting the The template specifies the binary learner model number of dimensions of expanded space kernel scale box constraint and regularization strength among other parameters. output by error correcting output codes model. 6819 Coding Lambda Learner Regularization _____ _____ _____ _____ onevsall 0. All the ECOC models were cross validated using k fold option with k 10. Specify 39 Regularization 39 of templatelinear as 39 lasso 39 to use lasso regularization. The lasso algorithm produces a smaller model with fewer predictors. utiliza 1 2 modelos de m quina vectorial de soporte binario SVM utilizando el uno contra uno donde est el n mero de etiquetas de clase nicas niveles . fitcecoc incorporates misclassification costs differently among different types of binary learners. Regularization 39 lasso 39 100 Regularization 39 bfgs 39 fitcsvm 1 2 SVM Machine learning Octave Tutorial Logistic Regression Regularization SVM Support Vector Machines Fitcdiscr matlab Stanford SVM Octave Tutorial Logistic Regression Regularization SVM Support Vector Machines Svm matlab Svm matlab Paperstac the digital mortgage note transaction engine. They claim that this can vastly speed up the training process and also acts as a sort of regularization. 1. There are a number of methods you can use for regularization so you should do some searching. Gamma provides finer control over the covariance matrix structure than DiscrimType. However it will not help much Matlab SVM Multiclass Function fitcecoc . pdf Text File . In such tasks both sensory responses in the brain and behavioral responses are highly variable. m Error correcting output codes while the hyper parameters such as regularization coeffiecient remains the default value. Board of Governors of the Federal Reserve System. For example enter Amount of regularization to apply when estimating the covariance matrix of the predictors specified as the comma separated pair consisting of 39 Gamma 39 and a scalar value in the interval 0 1 . to change a situation or system so that it obeys laws or is based on reason 2. txt or read online for free. trenes o valida cruzadamente un modelo de m quina vectorial de soporte SVM para la clasificaci n de una clase y dos clases binarias en un conjunto de datos predictor de baja dimensi n o de dimensi n moderada. Matlab Help Paperstac the digital mortgage note transaction engine. 92 begingroup You can compute the whole regularization path for a SVM in one go per Hastie et al 2004 the only remainder are the kernel hyperparameters. For nonlinear classification with big data train a binary Gaussian kernel classification model with regularized logistic regression using nbsp I wanted to know how I can tune the regularization parameters for 39 fitcecoc 39 to avoid overfitting the training set. Flexible functional interactions among brain regions mediate critical cognitive functions. BinaryLearner j . Therefore we don t want the optimizer to probe the range uniformly because it would waste time trying to distinguish between 0. models using fitcecoc . Jun 01 2019 After getting the waterfall spectrogram Matlab fitcecoc function trains an ECOC model composed of multiple binary linear classification models which are used to identify the waterfall spectrogram. Smola Learning with Kernels Support Vector Machines Regularization Optimization and Beyond The MIT Press 2002 nbsp fitcecoc ECOC. By the regularization objective and by the regularization term. Use the Matlab function 39 fitcecoc 39 . Principal component analysis PCA is a linear dimensionality reduction technique. A regression model that uses L1 regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression. MATLAB SVM 1 3604 2018 06 15 fitcecoc SVM Mdl fitcecoc tbl ResponseVarName tbl tbl ECOC Stanford . pdf Free ebook download as PDF File . Posterior 3 4 is the vector of all estimated posterior class probabilities for observation 3 using the model trained with regularization strength Lambda 4 . Stanford . For each fit function Mdl fitcecoc Tbl Y returns an ECOC model using the predictors in table Tbl and the class labels in vector Y. SVM 125389 2012 08 12 Machine learning Octave Tutorial Logistic Regression Regularization SVM Support Vector Machines MATLAB SVM 1 3680 2018 06 15 fitcecoc SVM Mdl fitcecoc tbl ResponseVarName tbl tbl ECOC Fitrensemble in matlab Fitcdiscr matlab Choose a Classifier MATLAB amp Simulink Free download as PDF File . However the performance is influenced by the size and degree of the problem. NumPy and SciPy were created to do numerical and scientific computing in the most natural way with Python not to be MATLAB clones. This is particularly due to the development of powerful new recording techniques such as optogenetics and transgenics in animal research Tonegawa et al. g. For example enter You can also efficiently train a multiclass error correcting output codes ECOC model composed of logistic regression models by using fitcecoc. MATLAB Central contributions by Shashank Gupta. SVM is a relatively new nonlinear technique in the field of chemometrics and it has been shown to perform well for classification tasks 2 regression 3 and time series prediction 4 . 2 4. Nov 15 2017 Brain Computer Interfaces BCIs decode brain activity with the aim to establish a direct communication channel with an external device. Getting more data is unlikely to properly solve the problem and is usually not worth the time. fitcecoc returns one CompactClassificationECOC model object containing ClassificationLinear binary learners but all binary learners contain a model for each regularization strength. Regularization applies to objective functions in ill posed optimization problems. fitcecoc uses K K nbsp Create a ClassificationECOC object by using fitcecoc . Learn more. To determine the quality of each regularized model pass the returned model object and the held out data to for example loss . Mdl fitcecoc Tbl formula returns an ECOC model using the predictors in table Tbl and the class labels. 4. csdn Regularization Neighborhood component analysis NCA 15 Multi class versions of the above fitcecoc Linear regression models fitlm Svm matlab. mathworks. 13244 0. 13224 0. The fitcecoc function takes feature vectors and class labels and returns a fully trained model using 1 2 binary SVM models where S is the number of unique class labels . com help stats fitcecoc. Between the goal of fitting the trading set well and the goal of keeping the parameters small and therefore keeping the hypothesis relatively simple to avoid overfitting. . Lasso or elastic net regularization for generalized linear models fitclinear Fit linear classification model to high dimensional data templateLinear Linear classification learner template fitcecoc Ajustar modelos multiclase para m quinas vectoriales de soporte u otros clasificadores predict Predict labels for linear classification Nov 15 2017 However several methods for covariance matrix estimation with a small number of samples have been described e. Lambda . May 31 2019 Author summary Establishing the link between neural activity and behavior is a central goal of neuroscience. One context in which to examine this link is in a sensory detection task in which an animal is trained to report the presence of a barely perceptible sensory stimulus. las funcionalidades de aprendizaje supervisado comprenden un marco de objetos forrado por secuencias. It is good practice to specify the type of decision tree e. Since there are n 4 classes we need to train total n n 1 2 6 binary one vs. If you specify linear or kernel binary learners without specifying cross validation options then fitcecoc nbsp 2020 6 28 Counts YTrain dataTrain2. html. fitcecoc predict fitckernel predict CVMdl fitcecoc X Y 39 Learners 39 t Name Value ECOC t 39 Linear 39 templateLinear MATLAB 5 100 2020 MATLAB 6 50 54 The lasso function incorporates both the lasso regularization algorithm and the elastic net regularization algorithm. There is a close connection to the 2 regularized logistic regression min. fitcecoc SVM Mdl fitcecoc tbl ResponseVarName tbl tbl ECOC Nov 25 2017 Matlab function fitcecoc with templateSVM for algorithm setting is available to implement the SVM algorithm. The lassoPlot function helps you visualize lasso results with a variety of coefficient trace plots and a cross validation plot. When training Mdl assume that you set 39 Standardize 39 true for a template object specified in the 39 Learners 39 name value pair argument of fitcecoc. 39 Deep Learning 39 13 Page Introduction. Ridge regression adds squared magnitude of coefficient as penalty term to the loss function. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information as much variance as possible. As a solution to this problem imaging fluorescence spectroscopy with additional illumination in the near infrared spectral range in Matlab How can I store the output of fitcecoc in a database matlab machine learning computer vision classification matlab cvst In Matlab help section there 39 s a very helpful example to solve classification problems under quot Digit Classification Using HOG Features quot . 4. In this case for the corresponding binary learner j the software standardizes the columns of the new predictor data using the corresponding means in Mdl. Furthermore a fitcecoc function that fits a multiclass model using the ECOC learning algorithm is implemented using one vs one default predictor first. Because the fMRI hemodynamic response is slow and is sampled at a timescale seconds several orders of magnitude slower than Regularization for linear models A squared penalty on the weights would make the math work nicely in our case 1 2 w y T w y 2 wTw This is also known as L2 regularization or weight decay in neural networks By re grouping terms we get J D w 1 2 wT T I w wT Ty yTw yTy Optimal solution obtained by solving r wJ D w 0 w T I Choose your classification or regression solver among fitcdiscr fitcecoc fitcensemble fitckernel fitcknn fitclinear fitcnb fitcsvm fitctree fitrensemble fitrgp fitrkernel fitrlinear fitrsvm or fitrtree. Mdl fitcecoc Tbl formula Tbl ECOC formula Tbl Lasso Regularization of Generalized Linear Models. formula is an Regularization 39 ridge 39 and 39 lasso 39 . L j is the average classification loss over all folds of the cross validated model that uses regularization strength j. 6 when in fact there s not much difference because both these values are too A 39 read 39 is counted each time someone views a publication summary such as the title abstract and list of authors clicks on a figure or views or downloads the full text. Sell mortgage notes faster buy mortgage notes easier. MdlFinal nbsp Platt J. fitcecoc for ECOC model classification If you specify a default decision tree template then the software uses default values for all input arguments during training. 2. Different mechanisms are available for multiclass classification in this paper one vs one or all vs all is adopted due to its simplicity and effectiveness 29 . 5 4. FitInfo 38 1 double FitInfoDescription 2 1 cell Regularization Properties Methods. Stanford SVM 2331 2012 12 13 Stanford SVM Octave Tutorial Logistic Regression Regularization SVM Support Vector Machines L 1 N i L i data loss R W regularization loss L 1 N i L i data loss R W regularization loss data loss Machine learning Octave Tutorial Logistic Regression Regularization SVM Support Vector Machines matlab 2015a matlab Stanford SVM 2333 2012 12 13 Stanford SVM Octave Tutorial Logistic Regression Regularization SVM Support Vector Machines 4 Jet 39 aime Quote 3 weixin_41109963 predict MATLAB SVM 1 3604 2018 06 15 fitcecoc SVM Mdl fitcecoc tbl ResponseVarName tbl tbl ECOC Error Correcting Output Codes ECOC represent a effective structure to handling with these kinds of problems. fitcsvm SVM fitcsvm SMO ISDA L1 fitcecoc returns one CompactClassificationECOC model object containing ClassificationLinear binary learners but all binary learners contain a model for each regularization strength. Do it if you can but it 39 s not a silver bullet that you can rely on to fix overfitting issues. They won a total of Rs 1500. 2 Definitions Comment for 1002. Choose Words to Recognize. I am using polynomial SVM in MATLAB for CIFAR 10 dataset using HOG features for data extraction. These plastics cannot yet be sorted with sufficient purity up to 99. Matlab How can I store the output of fitcecoc in a database matlab machine learning computer vision classification matlab cvst In Matlab help section there 39 s a very helpful example to solve classification problems under quot Digit Classification Using HOG Features quot . X D 1 2 . For example enter Contents. The interface of the function is Model fitcecoc x y learners t observationIn column where Jan 31 2018 The bound values are set to C 10 5 10 5 10 5 10 5 and the Matlab function fitcecoc is used to train multiple class SVM. Dec 01 2018 The template uses a default linear kernel function and the matrix is standardized. Use lasso to remove redundant or unimportant variables in linear regression. Remember that L2 amounts to adding a penalty on the norm of the weights to the loss. m is n by L where n is the number of observations in X and L is the number of regularization strengths in Mdl that is numel Mdl. L i j is the classification loss for fold i of the cross validated model that uses regularization strength j. Aug 22 2017 For the single binary classifiers in the classifier chain we combine logistic regression LR with Lasso regularization in order to select relevant features for subsequent classifier training this is essential as some of the parameters that are recorded for the purpose of permanent process supervision may not have a significant impact for which trains an SVM on the training set example_file and outputs the learned rule to model_file using the regularization parameter C set to 1. Choose a Classifier MATLAB amp Simulink Free download as PDF File . The Federal Reserve the central bank of the United States provides the nation with a safe flexible and stable monetary and financial system. for a classification tree template specify 39 Type 39 39 classification 39 . 1 Jul 2017 fitcecoc Fit multiclass models for support vector machines or other Because there are 11 regularization strengths ce is a 1 by 11 vector of fitcecoc. The fitcecoc regularization parameter also affects the accuracy of the learned classifier. We discuss the equivalence of the penalization and constraint forms of regularization s In your case regularization happened regardless of the lack of regularization contract assuming such is a standard practice of the company you are with. 2 Exemplar SVM on HOG nbsp Tune regularization parameters lambda alpha for xgboost which can help sin x x computing the values of sin x x compose plot pylab. These measures frequently incorporate regularization penalties to estimate For n way classification we used MATLAB 39 s fitcecoc function which is based on nbsp 12 Feb 2020 the svm optimization problem two regularization parameters and were tation at https it. Mdl fitcecoc ___ Name Value returns an ECOC model with additional options specified by one or more Name Value pair arguments Mdl fitcecoc Tbl formula Tbl ECOC formula Tbl Lasso Regularization of Generalized Linear Models. L2 Regularization. Mdl fitcecoc ___ Name Value returns an ECOC model with additional options specified by one or more Name Value pair arguments Mdl fitcecoc Tbl Y returns an ECOC model using the predictors in table Tbl and the class labels in vector Y. And what lambda the regularization parameter does is it controls the trade off between these two goals. trainrp Resilient backpropagation. Note that our algorithm uses two different cross validation procedures the first one is the Leave One Out cross validation LOO CV included in the selection of the regularization parameters of the GLMNet algorithm the second one is the subsampling of the variables and subjects in the step 2 of the loop in each iteration. The plot helps to. We introduce quot regularization quot our main defense against overfitting. Train SVM with i as ls lasso x y . regularization parameter gt 0 that controls the relative importance applied to the In our case we applied fitcecoc function 29 on the learned features from nbsp 23 Jul 2019 support vector machines fitcecoc a linear regression model mnrfit changing L1 and L2 regularisation in logistic regression or changing nbsp 25 Nov 2017 and constant K is regularization parameter controlling the penalty Matlab function fitcecoc with templateSVM for algorithm setting is nbsp various techniques like Fisher 39 s Discriminant Regularization and Distorted a classification model has been built using the command fitcecoc which returns a . A simple hypothesis Mdl fitcecoc Tbl Y returns an ECOC model using the predictors in table Tbl and the class labels in vector Y. 4 General Rules Comment for 1002. For more details see fitcecoc. 3 4. Y D 3 . It uses a one versus one all coding design. Using regularization usually solves overfitting issues. Mu and standard deviations in Mdl. 1 Authority Scope and Purpose Comment for 1002. CNNs are trained with backpropag ation in a supervised manner 18 . and specify the minimal regularization strength. 22 Mar 2016 regularization. 5 Rules Concerning Requests for Information Comment for 1002. Here C is the penalty parameter that represents misclassification or error. Train a linear classification model by using fitcecoc and linear binary learners defined by templateLinear. Press J to jump to the feed. Regularization C parameter used to maintain regularization. Mdl fitcecoc X Y returns a full trained ECOC model using the predictors X and the class labels Y. Sigma. l2_loss t . shrinkage Ledoit and Wolf 2004 regularization Friedman 1989 among others and can be assessed in terms of improving the estimation of the covariance matrix. Default dpca function parameters were used with parameters numRep 10 repetitions for regularization cross validation and simultaneous true indicating that the single trial neural data were simultaneously recorded across electrodes for the dpca_optimizeLambda and dpca_getNoiseCovariance functions. Exercise Ridge Regression middot Exercise Lasso Regression middot Exercise Fuel Economy Ridge Regression middot Exercise Fuel Economy nbsp C plays a role similar to 1 where is the regularization parameter that we were using previously for logistic regression. Oct 13 2017 L1 Regularization. I am an Application software engineer at Mathworks. For example you can specify that the columns of the predictor matrix correspond to observations implement logistic regression or specify to cross validate. Decide on the hyperparameters to optimize and pass them in the OptimizeHyperparameters name value pair. formula is an explanatory model of the response and a subset of predictor variables in Tbl used for training. Makes finely detailed distinctions between classes with kernel scale set to sqrt P 4. Mdl fitclinear X Y Name Value returns a trained linear classification model with additional options specified by one or more Name Value pair arguments. The related elastic net algorithm can be more accurate when predictors are highly correlated. Mdl fitcecoc Tbl formula returns an ECOC model using the predictors in table Tbl and the class labels. The template specifies the binary learner model regularization type and strength and solver among other things. 10 Apr 2012 Scholkopf and A. fitcecoc i o Train SVM with i as input and o as m ulti class. SVM matlab MATLAB SVM natlabfitsvm CSDN . trainrp. template templateSVM . Feb 10 2017 The Federal Reserve Board of Governors in Washington DC. SVM decision boundary. Mdl fitcecoc Tbl ResponseVarName devuelve un completo entrenado multiclase utilizando los predictores de la tabla y las etiquetas de clase en . For nonlinear classification with big data train a binary Gaussian kernel classification model with logistic regression by using fitckernel . 7 Rules Concerning Extensions of Credit The test pattern is assigned to the class with minimum Hamming distance which counts the number of bits that differ between the binary output code of the test pattern and base code of that class. 001. 6 Rules Concerning Evaluation of Applications Comment for 1002. J. for multi class problems and the so called multi class dis criminant principal components analysis Multi Class DPCA consists of the following steps a apply PCA technique for dimensionality reduction This work contributes to the recycling of technical black plastic particles for example from the automotive or electronics industries. 68209 Coding dropout layer augmentation of the input images or regularization methods like early stopping can be used. 6 4. It fits a multiclass SVM using error correcting output codes. 01 or 0. 2015 as well as the increasing application of multivariate analysis methods multivariate pattern analysis MVPA and representational similarity analysis RSA in human research. Lasso Regularization of Generalized Linear Models The lasso algorithm produces a smaller model with fewer predictors. In addition for the particular case analyzed in this paper multi ECOC technique or Fit multiclass models for support vector machines fitcecoc are used 21 . Feature Fusion Techniques 91 Results and Discussion 92 Segmentation Result 93 Feature Extraction Result 94 Conclusion 97 Future Work 99 Acknowledgment 99 t fitcecoc 1 ECOC t templateSVM Name Value 1 ClassificationPartitionedLinearECOC is a set of error correcting output codes ECOC models composed of linear classification models trained on cross validated folds. Trained another Linear SVM model in matlab using fitcecoc method on feature extracted using Histogram of Guide Prof. The classifier contains the number of categories and the category labels for the input imds images. fitcecoc regularization

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