An overview on semi-supervised support vector machine

Ding, Shifei; Zhu, Zhibin; Zhang, Xiekai

doi:10.1007/s00521-015-2113-7

Cited by 125 publications

(33 citation statements)

References 42 publications

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“…The most common styles of "learning" used for healthcare purposes include supervised, unsupervised, and deep learning [13,[36][37][38]. There are other ML methods like semi-supervised learning (blend of supervised and unsupervised) [39,40] and reinforcement learning where the algorithm acts as an agent in an interactive environment that learns by trial and error using rewards from its own actions and experiences [41].…”

Section: Machine Learning For Big Data Analysismentioning

confidence: 99%

Artificial Intelligence for Mental Health and Mental Illnesses: an Overview

Graham

Depp

Lee

et al. 2019

Curr Psychiatry Rep

494

260

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Purpose of Review Artificial intelligence (AI) technology holds both great promise to transform mental healthcare and potential pitfalls. This article provides an overview of AI and current applications in healthcare, a review of recent original research on AI specific to mental health, and a discussion of how AI can supplement clinical practice while considering its current limitations, areas needing additional research, and ethical implications regarding AI technology. Recent Findings We reviewed 28 studies of AI and mental health that used electronic health records (EHRs), mood rating scales, brain imaging data, novel monitoring systems (e.g., smartphone, video), and social media platforms to predict, classify, or subgroup mental health illnesses including depression, schizophrenia or other psychiatric illnesses, and suicide ideation and attempts. Collectively, these studies revealed high accuracies and provided excellent examples of AI's potential in mental healthcare, but most should be considered early proof-of-concept works demonstrating the potential of using machine learning (ML) algorithms to address mental health questions, and which types of algorithms yield the best performance. Summary As AI techniques continue to be refined and improved, it will be possible to help mental health practitioners re-define mental illnesses more objectively than currently done in the DSM-5, identify these illnesses at an earlier or prodromal stage when interventions may be more effective, and personalize treatments based on an individual's unique characteristics. However, caution is necessary in order to avoid over-interpreting preliminary results, and more work is required to bridge the gap between AI in mental health research and clinical care.

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Section: Machine Learning For Big Data Analysismentioning

confidence: 99%

Artificial Intelligence for Mental Health and Mental Illnesses: an Overview

Graham

Depp

Lee

et al. 2019

Curr Psychiatry Rep

494

260

View full text Add to dashboard Cite

show abstract

“…SVR has been widely used to approximate a nonlinear function by mapping input data into a higher dimensional feature space, in which the training data may exhibit linearity [14,15,16]. Given a set of l training points {(x1,y1), …,(xl,yl)}, where xi∈ℛn is an input vector of dimensionality n, yi∈ℛ is the target output.…”

Section: 3d Image Reconstruction Methodsmentioning

confidence: 99%

3D Reconstruction of Slug Flow in Mini-Channels with a Simple and Low-Cost Optical Sensor

Jiang

et al. 2019

Sensors

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The present work provides a new approach for 3D image reconstruction of gas-liquid two-phase flow (GLF) in mini-channels based on a new optical sensor. The sensor consists of a vertical and a horizontal photodiode array. Firstly, with the optical signals obtained by the vertical array, a measurement model developed by Support Vector Regression (SVR) was used to determine the cross-sectional information. The determined information was further used to reconstruct cross-sectional 2D images. Then, the gas velocity was calculated according to the signals obtained by the horizontal array, and the spatial interval of the 2D images was determined. Finally, 3D images were reconstructed by piling up the 2D images. In this work, the cross-sectional gas-liquid interface was considered as circular, and high-speed visualization was utilized to provide the reference values. The image deformation caused by channel wall was also considered. Experiments of slug flow in a channel with an inner diameter of 4.0 mm were carried out. The results verify the feasibility of the proposed 3D reconstruction method. The proposed method has the advantages of simple construct, low cost, and easily multipliable. The reconstructed 3D images can provide detailed and undistorted information of flow structure, which could further improve the measurement accuracy of other important parameters of gas-liquid two-phase flow, such as void fraction, pressure drop, and flow pattern.

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“…See Fig. 2 for a visualization of how the tsvm is intended to work and Ding et al [13] for a survey of semi-supervised svm's. Other methods for low-density separation include the more general entropy minimization approach [17], along with information regularization [39] and a Gaussian process-based approach [27].…”

Section: Low-density Separationmentioning

confidence: 99%

Deep Low-Density Separation for Semi-supervised Classification

Burkhart

Shan

2020

Lecture Notes in Computer Science

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Given a small set of labeled data and a large set of unlabeled data, semi-supervised learning (ssl) attempts to leverage the location of the unlabeled datapoints in order to create a better classifier than could be obtained from supervised methods applied to the labeled training set alone. Effective ssl imposes structural assumptions on the data, e.g. that neighbors are more likely to share a classification or that the decision boundary lies in an area of low density. For complex and high-dimensional data, neural networks can learn feature embeddings to which traditional ssl methods can then be applied in what we call hybrid methods. Previously-developed hybrid methods iterate between refining a latent representation and performing graph-based ssl on this representation. In this paper, we introduce a novel hybrid method that instead applies lowdensity separation to the embedded features. We describe it in detail and discuss why low-density separation may better suited for ssl on neural network-based embeddings than graph-based algorithms. We validate our method using in-house customer survey data and compare it to other state-of-the-art learning methods. Our approach effectively classifies thousands of unlabeled users from a relatively small number of hand-classified examples.

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An overview on semi-supervised support vector machine

Cited by 125 publications

References 42 publications

Artificial Intelligence for Mental Health and Mental Illnesses: an Overview

Artificial Intelligence for Mental Health and Mental Illnesses: an Overview

3D Reconstruction of Slug Flow in Mini-Channels with a Simple and Low-Cost Optical Sensor

Deep Low-Density Separation for Semi-supervised Classification

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