Optimizing different loss functions in multilabel classifications
نویسندگان
چکیده
منابع مشابه
Optimizing Complex Loss Functions in Structured Prediction
In this paper we develop an algorithm for structured prediction that optimizes against complex performance measures, those which are a function of false positive and false negative counts. The approach can be directly applied to performance measures such as Fβ score (natural language processing), intersection over union (image segmentation), Precision/Recall at k (search engines) and ROC area (...
متن کاملBayesian Estimation of Shift Point in Shape Parameter of Inverse Gaussian Distribution Under Different Loss Functions
In this paper, a Bayesian approach is proposed for shift point detection in an inverse Gaussian distribution. In this study, the mean parameter of inverse Gaussian distribution is assumed to be constant and shift points in shape parameter is considered. First the posterior distribution of shape parameter is obtained. Then the Bayes estimators are derived under a class of priors and using variou...
متن کاملAnalysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification
Top-k error is currently a popular performance measure on large scale image classification benchmarks such as ImageNet and Places. Despite its wide acceptance, our understanding of this metric is limited as most of the previous research is focused on its special case, the top-1 error. In this work, we explore two directions that shed light on the top-k error. First, we provide an in-depth analy...
متن کاملClassifications of Different Trends ( Extended
The most famous, and simple suboptimal search algorithm is Weighted A∗ (WA∗) (Pohl 1970). Similar to A∗, WA∗ uses two data structures: OPEN and CLOSED. Initially, OPEN contains only the start state and CLOSED is empty. At every iteration, the algorithm chooses the state in OPEN that minimizes the cost function f(n) = g(n)+W ·h(n), where g(n) is the cost of the lowest cost path found so far, fro...
متن کاملMultilabel Prediction with Probability Sets: The Hamming Loss Case
In this paper, we study how multilabel predictions can be obtained when our uncertainty is described by a convex set of probabilities. Such predictions, typically consisting of a set of potentially optimal decisions, are hard to make in large decision spaces such as the one considered in multilabel problems. However, we show that when considering the Hamming loss, an approximate prediction can ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Progress in Artificial Intelligence
سال: 2014
ISSN: 2192-6352,2192-6360
DOI: 10.1007/s13748-014-0060-7