StructED: Risk Minimization in Structured Prediction
نویسندگان
چکیده
Structured tasks are distinctive: each task has its own measure of performance, such as the word error rate in speech recognition, the BLEU score in machine translation, the NDCG score in information retrieval, or the intersection-over-union score in visual object segmentation. This paper presents StructED, a software package for learning structured prediction models with training methods that aimed at optimizing the task measure of performance. The package was written in Java and released under the MIT license. It can be downloaded from http://adiyoss.github.io/StructED/.
منابع مشابه
Risk Minimization in Structured Prediction using Orbit Loss
We introduce a new surrogate loss function called orbit loss in the structured prediction framework, which has good theoretical and practical advantages. While the orbit loss is not convex, it has a simple analytical gradient and a simple perceptron-like learning rule. We analyze the new loss theoretically and state a PAC-Bayesian generalization bound. We also prove that the new loss is consist...
متن کاملDistributed Block-diagonal Approximation Methods for Regularized Empirical Risk Minimization
Designing distributed algorithms for empirical risk minimization (ERM) has become an active research topic in recent years because of the practical need to deal with the huge volume of data. In this paper, we propose a general framework for training an ERM model via solving its dual problem in parallel over multiple machines. Our method provides a versatile approach for many large-scale machine...
متن کاملStructured Prediction Theory and Voted Risk Minimization
We present a general theoretical analysis of structured prediction with a series of new results. We give new data-dependent margin guarantees for structured prediction for a very wide family of loss functions and a general family of hypotheses, with an arbitrary factor graph decomposition. These are the tightest margin bounds known for both standard multi-class and general structured prediction...
متن کاملTopics in Structured Prediction: Problems and Approaches
We consider the task of structured data prediction. Over the last few years, there has been an abundance of data having inherent structure with strong correlation and complex dependencies between different parts of each input. Numerous applications across different disciplines like Part Of Speech tagging, Optical Character Recognition, Pitch accent prediction among others underline the structur...
متن کاملOn Structured Prediction Theory with Calibrated Convex Surrogate Losses
We provide novel theoretical insights on structured prediction in the context of efficient convex surrogate loss minimization with consistency guarantees. For any task loss, we construct a convex surrogate that can be optimized via stochastic gradient descent and we prove tight bounds on the so-called “calibration function” relating the excess surrogate risk to the actual risk. In contrast to p...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Journal of Machine Learning Research
دوره 17 شماره
صفحات -
تاریخ انتشار 2016