BinaryLoss— Binary learner loss function "hamming" | "linear" | "logit" | "exponential" | "binodeviance" | "hinge" | "quadratic" | function handle Decoding— Decoding scheme "lossweighted" (default) | "lossbased" Options— Estimation options [] (default) | structure array Verbose— Verbos...
The software composes the objective function for minimization from the sum of the average loss function (see Learner) and the regularization term in this table. ValueDescription 'lasso' Lasso (L1) penalty: λp∑j=1∣βj∣ 'ridge' Ridge (L2) penalty: λ2p∑j=1β2j To specify the regula...
Loss Function quantifies the error between predicted and actual values in a machine learning model. It guides the model's training by indicating how well it's performing. Log Likelihood Function representing how similar robot behavior is to human behavior L L = ∑ B L × log P L +...
'KernelFunction','rbf','BoxConstraint',x.BoxConstraint, ... 'KernelScale',x.KernelScale,'Standardize',x.Standardize=='true')) L_MinEstimated = 0.0700 The actual cross-validated loss is close to the estimated value. The Estimated objective function value is displayed below the plot of the...
Objective function minimization technique: 'LBFGS-fast', 'LBFGS-blockwise', or 'LBFGS-tall'. For details, see Algorithms. LossFunction Loss function. Either 'hinge' or 'logit' depending on the type of linear classification model. See Learner. Lambda Regularization term strength. See Lambda. Be...
Find hyperparameters that minimize five-fold cross-validation loss by using automatic hyperparameter optimization. For reproducibility, set the random seed and use the'expected-improvement-plus'acquisition function. Get rngdefaultMdl = fitcsvm(X,Y,'OptimizeHyperparameters','auto',...'HyperparameterOptim...
Convergence is underwritten by periodically enforcing synchronization between primal and dual variables in a separate thread. Several choices of loss functions are also provided such ashinge-lossandlogistic loss. Depending on the loss used, the trained model can be, for example,support vector machineor...
fitclinear fits a ClassificationLinear model by minimizing the objective function using techniques that reduce computation time for high-dimensional data sets (e.g., stochastic gradient descent). The classification loss plus the regularization term compose the objective function. Unlike other classification...
(BOH), in which we prove that if the loss function is Lipschitz continuous, the binary optimization problem can be relaxed to a bound-constrained continuous optimization problem. Then we introduce a surrogate objective function, which only depends on unbinarized hash functions and does not need ...
Find the optimal 'MinLeafSize' value that minimizes holdout cross-validation loss. (Specifying 'auto' uses 'MinLeafSize'.) For reproducibility, use the 'expected-improvement-plus' acquisition function and set the seeds of the random number generators using rng and tallrng. The results can vary...