‘hinge’ is the standard SVM loss (used e.g. method a character string specifying the loss function to use, valid options are: • "hhsvm" Huberized squared hinge loss, • "sqsvm" Squared hinge loss, • "logit" logistic loss, • "ls" least square loss. However, when yf(x) < 1, then hinge loss increases massively. by the SVC class) while ‘squared_hinge’ is the square of the hinge loss. It is purely problem specific. 指数损失（Exponential Loss） ：主要用于Adaboost 集成学习算法中； 5. Hinge Loss. Understanding Ranking Loss, Contrastive Loss, Margin Loss, Triplet Loss, Hinge Loss and all those confusing names. The x-axis represents the distance from the boundary of any single instance, and the y-axis represents the loss size, or penalty, that the function will incur depending on its distance. Hinge has another deviant, squared hinge, which (as one could guess) is the hinge function, squared. • "er" expectile regression loss. Square loss is more commonly used in regression, but it can be utilized for classification by re-writing as a function . LinearSVC is actually minimizing squared hinge loss, instead of just hinge loss, furthermore, it penalizes size of the bias (which is not SVM), for more details refer to other question: Under what parameters are SVC and LinearSVC in scikit-learn equivalent? Default is "hhsvm". Here is a really good visualisation of what it looks like. The hinge loss is a loss function used for training classifiers, most notably the SVM. Theorem 2. #FOR COMPILING model.compile(loss='squared_hinge', optimizer='sgd') # optimizer can be substituted for another one #FOR EVALUATING keras.losses.squared_hinge(y_true, y_pred) The combination of penalty='l1' and loss='hinge' is not supported. The square loss function is both convex and smooth and matches the 0–1 when and when . 平方损失（Square Loss）：主要是最小二乘法（OLS）中； 4. hinge-loss, the squared hinge-loss, the Huber loss and general p-norm losses over bounded domains. loss {‘hinge’, ‘squared_hinge’}, default=’squared_hinge’ Specifies the loss function. Apr 3, 2019. Last week, we discussed Multi-class SVM loss; specifically, the hinge loss and squared hinge loss functions.. A loss function, in the context of Machine Learning and Deep Learning, allows us to quantify how “good” or “bad” a given classification function (also called a “scoring function”) is at correctly classifying data points in our dataset. So which one to use? Hinge loss 的叫法来源于其损失函数的图形，为一个折线，通用的函数表达式为： Square Loss. 其他损失（如0-1损失，绝对值损失） 2.1 Hinge loss. The hinge loss is used for maximum-margin classification task, most notably for support vector machines (SVMs). 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