scholarly journals Optimal Margin Distribution Machine for Multi-Instance Learning

2020 ◽  
Author(s):  
Teng Zhang ◽  
Hai Jin
Keyword(s):  
Minimax Problem ◽  
Positive Instance ◽  
Large Margin ◽  
Generalization Ability ◽  
Learning Framework ◽  
The Past ◽  
Margin Distribution

Multi-instance learning (MIL) is a celebrated learning framework where each example is represented as a bag of instances. An example is negative if it has no positive instances, and vice versa if at least one positive instance is contained. During the past decades, various MIL algorithms have been proposed, among which the large margin based methods is a very popular class. Recently, the studies on margin theory disclose that the margin distribution is of more importance to generalization ability than the minimal margin. Inspired by this observation, we propose the multi-instance optimal margin distribution machine, which can identify the key instances via explicitly optimizing the margin distribution. We also extend a stochastic accelerated mirror prox method to solve the formulated minimax problem. Extensive experiments show the superiority of the proposed method.

MULFE: Multi-Label Learning via Label-Specific Feature Space Ensemble

2021 ◽  
Vol 16 (1) ◽  
pp. 1-24
Author(s):  
Yaojin Lin ◽  
Qinghua Hu ◽  
Jinghua Liu ◽  
Xingquan Zhu ◽  
Xindong Wu
Keyword(s):  
Empirical Studies ◽  
Feature Space ◽  
Training Data ◽  
Data Sets ◽  
Learning Framework ◽  
Feature Spaces ◽  
Public Data ◽  
Margin Distribution ◽  
Label Correlations ◽  
Label Correlation

In multi-label learning, label correlations commonly exist in the data. Such correlation not only provides useful information, but also imposes significant challenges for multi-label learning. Recently, label-specific feature embedding has been proposed to explore label-specific features from the training data, and uses feature highly customized to the multi-label set for learning. While such feature embedding methods have demonstrated good performance, the creation of the feature embedding space is only based on a single label, without considering label correlations in the data. In this article, we propose to combine multiple label-specific feature spaces, using label correlation, for multi-label learning. The proposed algorithm, mu lti- l abel-specific f eature space e nsemble (MULFE), takes consideration label-specific features, label correlation, and weighted ensemble principle to form a learning framework. By conducting clustering analysis on each label’s negative and positive instances, MULFE first creates features customized to each label. After that, MULFE utilizes the label correlation to optimize the margin distribution of the base classifiers which are induced by the related label-specific feature spaces. By combining multiple label-specific features, label correlation based weighting, and ensemble learning, MULFE achieves maximum margin multi-label classification goal through the underlying optimization framework. Empirical studies on 10 public data sets manifest the effectiveness of MULFE.

scholarly journals Feature Extraction for Finger-Vein-Based Identity Recognition

2021 ◽  
Vol 7 (5) ◽  
pp. 89
Author(s):  
George K. Sidiropoulos ◽  
Polixeni Kiratsa ◽  
Petros Chatzipetrou ◽  
George A. Papakostas
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Methods ◽  
Identity Recognition ◽  
Learning Framework ◽  
Finger Vein ◽  
The Past ◽  
Biometric Systems ◽  
Vein Recognition ◽  
Finger Vein Recognition

This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on finger vein biometric features. The analysis spans over a period of 13 years (from 2008 to 2020). The examined feature extraction algorithms are clustered into five categories and are presented in a qualitative manner by focusing mainly on the techniques applied to represent the features of the finger veins that uniquely prove a human’s identity. In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. The conducted literature analysis revealed the increased interest in finger vein biometric systems as well as the high diversity of different feature extraction methods proposed over the past several years. However, last year this interest shifted to the application of Convolutional Neural Networks following the general trend of applying deep learning models in a range of disciplines. Finally, yet importantly, this work highlights the limitations of the existing feature extraction methods and describes the research actions needed to face the identified challenges.

Ensemble Pruning for Data Dependant Learners

2011 ◽  
Vol 135-136 ◽  
pp. 522-527 ◽  
Author(s):  
Gang Zhang ◽  
Shan Hong Zhan ◽  
Chun Ru Wang ◽  
Liang Lun Cheng
Keyword(s):  
Unlabeled Data ◽  
Experimental Results ◽  
Clear Relationship ◽  
Data Sets ◽  
Ensemble Pruning ◽  
Generalization Ability ◽  
Learning Framework ◽  
Single Instance ◽  
Better Than ◽  
Individual Kernel

Ensemble pruning searches for a selective subset of members that performs as well as, or better than ensemble of all members. However, in the accuracy / diversity pruning framework, generalization ability of target ensemble is not considered, and moreover, there is not clear relationship between them. In this paper, we proof that ensemble formed by members of better generalization ability is also of better generalization ability. We adopt learning with both labeled and unlabeled data to improve generalization ability of member learners. A data dependant kernel determined by a set of unlabeled points is plugged in individual kernel learners to improve generalization ability, and ensemble pruning is launched as much previous work. The proposed method is suitable for both single-instance and multi-instance learning framework. Experimental results on 10 UCI data sets for single-instance learning and 4 data sets for multi-instance learning show that subensemble formed by the proposed method is effective.

scholarly journals Lifelong Spectral Clustering

2020 ◽  
Vol 34 (04) ◽  
pp. 5867-5874
Author(s):  
Gan Sun ◽  
Yang Cong ◽  
Qianqian Wang ◽  
Jun Li ◽  
Yun Fu
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Spectral Clustering ◽  
State Of The Art ◽  
Clustering Algorithms ◽  
Orthogonal Basis ◽  
Learning Framework ◽  
The Past ◽  
Benchmark Datasets ◽  
Over Time

In the past decades, spectral clustering (SC) has become one of the most effective clustering algorithms. However, most previous studies focus on spectral clustering tasks with a fixed task set, which cannot incorporate with a new spectral clustering task without accessing to previously learned tasks. In this paper, we aim to explore the problem of spectral clustering in a lifelong machine learning framework, i.e., Lifelong Spectral Clustering (L2SC). Its goal is to efficiently learn a model for a new spectral clustering task by selectively transferring previously accumulated experience from knowledge library. Specifically, the knowledge library of L2SC contains two components: 1) orthogonal basis library: capturing latent cluster centers among the clusters in each pair of tasks; 2) feature embedding library: embedding the feature manifold information shared among multiple related tasks. As a new spectral clustering task arrives, L2SC firstly transfers knowledge from both basis library and feature library to obtain encoding matrix, and further redefines the library base over time to maximize performance across all the clustering tasks. Meanwhile, a general online update formulation is derived to alternatively update the basis library and feature library. Finally, the empirical experiments on several real-world benchmark datasets demonstrate that our L2SC model can effectively improve the clustering performance when comparing with other state-of-the-art spectral clustering algorithms.

scholarly journals Sickness Detection on the Leaves of the Tomato Plants by using Deep Learning

2020 ◽  
Vol 9 (8) ◽  
pp. 751-757
Keyword(s):  
Tomato Leaf ◽  
Learning Systems ◽  
Tomato Plants ◽  
Learning Framework ◽  
The Past ◽  
Standard Component ◽  
Component Extraction ◽  
Physical Changes ◽  
Learning Frameworks ◽  
The Web

The purpose of this work is to recognize diseases that occur on plants in tomato fields or in their nurseries. Thus, significant learning was used to perceive the various sicknesses on the leaves of tomato plants. In the assessment, it was pointed that the significant learning figuring should be run ceaselessly on the robot. So the robot will have the alternative to perceive the ailments of the plants while wandering truly or of course self-rulingly on the field or in the nursery. Also, illnesses can in like manner be recognized from close-up photographs taken from plants by sensors worked in produced nurseries. The assessed diseases in this assessment cause physical changes in the leaves of the tomato plant. These movements on the leaves can be seen with RGB cameras. In the past examinations, standard component extraction strategies on plant leaf pictures to perceive disorders have been used. In this assessment, significant learning systems were used to perceive disorders. Significant getting the hang of building decision was the key issue for the execution. So that, two unmistakable significant learning framework models were attempted first AlexNet and thereafter SqueezeNet. For both of these significant learning frameworks getting ready and endorsement were done on the Nvidia Jetson TX1. Tomato leaf pictures from the PlantVillage dataset has been used for the readiness. Ten unmistakable classes including sound pictures are used. Arranged frameworks are moreover taken a stab at the photos from the web.

Analyzing and Integrating Dependency Parsers

2011 ◽  
Vol 37 (1) ◽  
pp. 197-230 ◽  
Author(s):  
Ryan McDonald ◽  
Joakim Nivre
Keyword(s):  
Word Order ◽  
Integrated System ◽  
Data Driven ◽  
Dependency Parsing ◽  
Learning Framework ◽  
The Past ◽  
Complex Phenomena ◽  
Free Word

There has been a rapid increase in the volume of research on data-driven dependency parsers in the past five years. This increase has been driven by the availability of treebanks in a wide variety of languages—due in large part to the CoNLL shared tasks—as well as the straightforward mechanisms by which dependency theories of syntax can encode complex phenomena in free word order languages. In this article, our aim is to take a step back and analyze the progress that has been made through an analysis of the two predominant paradigms for data-driven dependency parsing, which are often called graph-based and transition-based dependency parsing. Our analysis covers both theoretical and empirical aspects and sheds light on the kinds of errors each type of parser makes and how they relate to theoretical expectations. Using these observations, we present an integrated system based on a stacking learning framework and show that such a system can learn to overcome the shortcomings of each non-integrated system.

Sign in / Sign up

Export Citation Format

Share Document