Multi-instance dictionary learning via multivariate performance measure optimization
نویسندگان
چکیده
منابع مشابه
Scalable Optimization of Multivariate Performance Measures in Multi-instance Multi-label Learning
The problem of multi-instance multi-label learning (MIML) requires a bag of instances to be assigned a set of labels most relevant to the bag as a whole. The problem finds numerous applications in machine learning, computer vision, and natural language processing settings where only partial or distant supervision is available. We present a novel method for optimizing multivariate performance me...
متن کاملScalable Optimization of Multivariate Performance Measures in Multi-instance Multi-label Learning
The problem of multi-instance multi-label learning (MIML) requires a bag of instances to be assigned a set of labels most relevant to the bag as a whole. The problem finds numerous applications in machine learning, computer vision, and natural language processing settings where only partial or distant supervision is available. We present a novel method for optimizing multivariate performance me...
متن کاملOnline Multi-Task Learning via Sparse Dictionary Optimization
This paper develops an efficient online algorithm for learning multiple consecutive tasks based on the KSVD algorithm for sparse dictionary optimization. We first derive a batch multi-task learning method that builds upon K-SVD, and then extend the batch algorithm to train models online in a lifelong learning setting. The resulting method has lower computational complexity than other current li...
متن کاملMulti-view learning for multivariate performance measures optimization
In this paper, we propose the problem of optimizing multivariate performance measures from multi-view data, and an effective method to solve it. This problem has two features: the data points are presented by multiple views, and the target of learning is to optimize complex multivariate performance measures. We propose to learn a linear discriminant functions for each view, and combine them to ...
متن کاملLearning Instance Weights in Multi-Instance Learning
Multi-instance (MI) learning is a variant of supervised machine learning, where each learning example contains a bag of instances instead of just a single feature vector. MI learning has applications in areas such as drug activity prediction, fruit disease management and image classification. This thesis investigates the case where each instance has a weight value determining the level of influ...
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: Pattern Recognition
سال: 2017
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2016.12.023