Local uncertainty sampling for large-scale multiclass logistic regression
نویسندگان
چکیده
منابع مشابه
Local Uncertainty Sampling for Large-Scale Multi-Class Logistic Regression
A major challenge for building statistical models in the big data era is that the available data volume may exceed the computational capability. A common approach to solve this problem is to employ a subsampled dataset that can be handled by the available computational resources. In this paper, we propose a general subsampling scheme for large-scale multi-class logistic regression, and examine ...
متن کاملLarge Scale Distributed Multiclass Logistic Regression
Multiclass logistic regression (MLR) is a fundamental machine learning model to do multiclass classification. However, it is very challenging to perform MLR on large scale data where the feature dimension is high, the number of classes is large and the number of data samples is numerous. In this paper, we build a distributed framework to support large scale multiclass logistic regression. Using...
متن کاملLocal Logistic Classifiers for Large Scale Learning
This article describes the construction of the local logistic classifier, a highly scalable and parallelizable classifier combining local learning with a logistic classifier. To speed up the learning of each local logistic classifier, a novel fixed point algorithm is introduced. The practicality and performance of the classifier is demonstrated on a 10 million character training set derived fro...
متن کاملEfficient Online Learning for Large-Scale Sparse Kernel Logistic Regression
In this paper, we study the problem of large-scale Kernel Logistic Regression (KLR). A straightforward approach is to apply stochastic approximation to KLR. We refer to this approach as non-conservative online learning algorithm because it updates the kernel classifier after every received training example, leading to a dense classifier. To improve the sparsity of the KLR classifier, we propose...
متن کاملA Method for Large-Scale l1-Regularized Logistic Regression
Logistic regression with l1 regularization has been proposed as a promising method for feature selection in classification problems. Several specialized solution methods have been proposed for l1-regularized logistic regression problems (LRPs). However, existing methods do not scale well to large problems that arise in many practical settings. In this paper we describe an efficient interior-poi...
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: The Annals of Statistics
سال: 2020
ISSN: 0090-5364
DOI: 10.1214/19-aos1867