Survey of Multi Instance learning Algorithms
نویسندگان
چکیده
منابع مشابه
Multi-Instance Learning: A Survey
In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. This paper provides a survey on this topic. At first, it introduces the origin of multi-instance learning. Then, developments on the study of learnability, learning algorithms, applications and extensions of multi-instance learning a...
متن کاملA Comparison of Multi-instance Learning Algorithms
Motivated by various challenging real-world applications, such as drug activity prediction and image retrieval, multi-instance (MI) learning has attracted considerable interest in recent years. Compared with standard supervised learning, the MI learning task is more difficult as the label information of each training example is incomplete. Many MI algorithms have been proposed. Some of them are...
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 کاملStudy of Different Multi-instance Learning kNN Algorithms
Because of it is applicability in various field, multi-instance learning or multi-instance problem becoming more popular in machine learning research field. Different from supervised learning, multi-instance learning related to the problem of classifying an unknown bag into positive or negative label such that labels of instances of bags are ambiguous. This paper uses and study three different ...
متن کاملApplying propositional learning algorithms to multi-instance data
Multi-instance learning is commonly tackled using special-purpose algorithms. Development of these algorithms has started because early experiments with standard propositional learners have failed to produce satisfactory results on multi-instance data—more specifically, the Musk data. In this paper we present evidence that this is not necessarily the case. We introduce a simple wrapper for appl...
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: IJARCCE
سال: 2018
ISSN: 2319-5940,2278-1021
DOI: 10.17148/ijarcce.2018.7811