Abstract
Supervised learning techniques construct predictive models by learning from a large number of training examples, where each training example has a label indicating its ground-truth output. ...Though current techniques have achieved great success, it is noteworthy that in many tasks it is difficult to get strong supervision information like fully ground-truth labels due to the high cost of the data-labeling process. Thus, it is desirable for machine-learning techniques to work with weak supervision. This article reviews some research progress of weakly supervised learning, focusing on three typical types of weak supervision: incomplete supervision, where only a subset of training data is given with labels; inexact supervision, where the training data are given with only coarse-grained labels; and inaccurate supervision, where the given labels are not always ground-truth.
A Review on Multi-Label Learning Algorithms Zhang, Min-Ling; Zhou, Zhi-Hua
IEEE transactions on knowledge and data engineering,
08/2014, Letnik:
26, Številka:
8
Journal Article
Recenzirano
Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of ...progresses have been made toward this emerging machine learning paradigm. This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms. Firstly, fundamentals on multi-label learning including formal definition and evaluation metrics are given. Secondly and primarily, eight representative multi-label learning algorithms are scrutinized under common notations with relevant analyses and discussions. Thirdly, several related learning settings are briefly summarized. As a conclusion, online resources and open research problems on multi-label learning are outlined for reference purposes.
While recognition of most facial variations, such as identity, expression, and gender, has been extensively studied, automatic age estimation has rarely been explored. In contrast to other facial ...variations, aging variation presents several unique characteristics which make age estimation a challenging task. This paper proposes an automatic age estimation method named AGES (AGing pattErn Subspace). The basic idea is to model the aging pattern, which is defined as the sequence of a particular individual's face images sorted in time order, by constructing a representative subspace. The proper aging pattern for a previously unseen face image is determined by the projection in the subspace that can reconstruct the face image with minimum reconstruction error, while the position of the face image in that aging pattern will then indicate its age. In the experiments, AGES and its variants are compared with the limited existing age estimation methods (WAS and AAS) and some well-established classification methods (kNN, BP, C4.5, and SVM). Moreover, a comparison with human perception ability on age is conducted. It is interesting to note that the performance of AGES is not only significantly better than that of all the other algorithms, but also comparable to that of the human observers.
Deep forest Zhou, Zhi-Hua; Feng, Ji
National science review,
01/2019, Letnik:
6, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Abstract
Current deep-learning models are mostly built upon neural networks, i.e. multiple layers of parameterized differentiable non-linear modules that can be trained by backpropagation. In this ...paper, we explore the possibility of building deep models based on non-differentiable modules such as decision trees. After a discussion about the mystery behind deep neural networks, particularly by contrasting them with shallow neural networks and traditional machine-learning techniques such as decision trees and boosting machines, we conjecture that the success of deep neural networks owes much to three characteristics, i.e. layer-by-layer processing, in-model feature transformation and sufficient model complexity. On one hand, our conjecture may offer inspiration for theoretical understanding of deep learning; on the other hand, to verify the conjecture, we propose an approach that generates deep forest holding these characteristics. This is a decision-tree ensemble approach, with fewer hyper-parameters than deep neural networks, and its model complexity can be automatically determined in a data-dependent way. Experiments show that its performance is quite robust to hyper-parameter settings, such that in most cases, even across different data from different domains, it is able to achieve excellent performance by using the same default setting. This study opens the door to deep learning based on non-differentiable modules without gradient-based adjustment, and exhibits the possibility of constructing deep models without backpropagation.
It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances; or that ...the label correlations are local and shared only by a data subset. In fact, in the real-world applications, both cases may occur that some label correlations are globally applicable and some are shared only in a local group of instances. Moreover, it is also a usual case that only partial labels are observed, which makes the exploitation of the label correlations much more difficult. That is, it is hard to estimate the label correlations when many labels are absent. In this paper, we propose a new multi-label approach GLOCAL dealing with both the full-label and the missing-label cases, exploiting global and local label correlations simultaneously, through learning a latent label representation and optimizing label manifolds. The extensive experimental studies validate the effectiveness of our approach on both full-label and missing-label data.
Performing CO
2
conversion in a cost-effective and environmentally benign manner would be promising and remains challenging due to its thermodynamic stability and kinetic inertness. Herein, we would ...like to summarise significant advances in organic synthesis using CO
2
with high catalytic efficiency and excellent selectivity towards the target product mainly during the last five years (2012-2016). Achieving an efficient and selective CO
2
conversion depends on the development of metal catalysts (especially functional metal complex catalysis) including main-group metal, typical transition metal and lanthanide series metal as well as organocatalysts
e.g.
N-heterocyclic carbenes, N-heterocyclic olefins, task-specific ionic liquids, superbases and frustrated Lewis pairs that are able to effectively activate CO
2
and/or the substrate on the basis of the mechanistic understanding at the molecular level. This review just covers typical catalytic transformation of CO
2
, for instance, carboxylation, amidation, hydrogenation, and representative green processes like solvent-less, halogen-free that use CO
2
as an ideal carbon-neutral source to prepare valuable compounds with improved atom economy and enhanced sustainability of chemical processes through green catalysis. In particular,
in situ
catalytic CO
2
conversion,
i.e.
the combination of carbon capture and subsequent conversion, a recent breakthrough in the CO
2
chemistry field, is also discussed.
The efficient and selective conversion of CO
2
as a sustainable C
1
resource into valuable chemicals and energy-related products through catalysis is reviewed.
Methotrexate (MTX) is used as an anchor disease-modifying anti-rheumatic drugs (DMARDs) in treating rheumatoid arthritis (RA) because of its potent efficacy and tolerability. MTX benefits a large ...number of RA patients but partially suffered from side effects. A variety of side effects can be associated with MTX when treating RA patients, from mild to severe or discontinuation of the treatment. In this report, we reviewed the possible side effects that MTX might cause from the most common gastrointestinal toxicity effects to less frequent malignant diseases. In order to achieve regimen with less side effects, the administration of MTX with appropriate dose and a careful pretreatment inspection is necessary. Further investigations are required when combining MTX with other drugs so as to enhance the efficacy and reduce side effects at the same time. The management of MTX treatment is also discussed to provide strategies for occurred side effects. Thus, this review will provide scholars with a comprehensive understanding the side effects of MTX administration by RA patients.
Display omitted Risk factors of MTX induced side effects and their relationship.
•EULAR regarded Methotrexate as the first choice conventional synthetic DMARD for RA treatment.•RA patients who accept MTX therapy have different toxic reactions.•Measures could be taken to reduce MTX induced side effects.
Topological insulators are materials that behave as insulators in the bulk and as conductors at the edge or surface due to the particular configuration of their bulk band dispersion. However, up to ...date possible practical applications of this band topology on materials' bulk properties have remained abstract. Here, we propose and experimentally demonstrate a topological bulk laser. We pattern semiconductor nanodisk arrays to form a photonic crystal cavity showing topological band inversion between its interior and cladding area. In-plane light waves are reflected at topological edges forming an effective cavity feedback for lasing. This band-inversion-induced reflection mechanism induces single-mode lasing with directional vertical emission. Our topological bulk laser works at room temperature and reaches the practical requirements in terms of cavity size, threshold, linewidth, side-mode suppression ratio and directionality for most practical applications according to Institute of Electrical and Electronics Engineers and other industry standards. We believe this bulk topological effect will have applications in near-field spectroscopy, solid-state lighting, free-space optical sensing and communication.
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