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： 指数损失（Exponential Loss） ：主要用于Adaboost 集成学习算法中； 5. Square Loss. The square loss function is both convex and smooth and matches the 0–1 when and when . Hinge has another deviant, squared hinge, which (as one could guess) is the hinge function, squared. loss {‘hinge’, ‘squared_hinge’}, default=’squared_hinge’ Specifies the loss function. ‘hinge’ is the standard SVM loss (used e.g. Understanding Ranking Loss, Contrastive Loss, Margin Loss, Triplet Loss, Hinge Loss and all those confusing names. 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? Theorem 2. Square loss is more commonly used in regression, but it can be utilized for classification by re-writing as a function . Hinge Loss. Apr 3, 2019. 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. The hinge loss is a loss function used for training classifiers, most notably the SVM. It is purely problem specific. by the SVC class) while ‘squared_hinge’ is the square of the hinge loss. • "er" expectile regression loss. 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. hinge-loss, the squared hinge-loss, the Huber loss and general p-norm losses over bounded domains. There are several different common loss functions to choose from: the cross-entropy loss, the mean-squared error, the huber loss, and the hinge loss – just to name a few.” Some Thoughts About The Design Of Loss Functions (Paper) – “The choice and design of loss functions is discussed. 平方损失（Square Loss）：主要是最小二乘法（OLS）中； 4. So which one to use? However, when yf(x) < 1, then hinge loss increases massively. 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. After the success of my post Understanding Categorical Cross-Entropy Loss, Binary Cross-Entropy Loss, Softmax Loss, Logistic Loss, Focal Loss and all those confusing names, and after checking that Triplet Loss outperforms Cross-Entropy Loss in my main research … The combination of penalty='l1' and loss='hinge' is not supported. 其他损失（如0-1损失，绝对值损失） 2.1 Hinge loss. #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) Let I denote the set of rounds at which the Perceptron algorithm makes an update when processing a sequence of training in-stances x The hinge loss is used for maximum-margin classification task, most notably for support vector machines (SVMs). Default is "hhsvm". Here is a really good visualisation of what it looks like. dual bool, default=True But it can be utilized for classification by re-writing as a function Huber loss and all confusing! 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