Semi-Supervised Learning Based on Manifold in BCI

Abstract-A Laplacian support vector machine （LapSVM） algorithm, a semi-supervised learning based on manifold, is introduced to brain-computer interface （BCI） to raise the classification precision and reduce the subjects＇ training complexity. The data are collected from three subjects in a three-task mental imagery experiment. LapSVM and transductive SVM （TSVM） are trained with a few labeled samples and a large number of unlabeled samples. The results confirm that LapSVM has a much better classification than TSVM.     

Automatic Image Annotation Using Semi-Supervised Multi-Instance Multi-Label Learning Algorithm

Automatic image annotation has emerged as an important research topic. From the perspective of machine learning, the annotation task fits both multiinstance and multi-label learning framework due to the fact that an image is composed of multiple regions, and is associated with multiple keywords as well. In this paper, we propose a novel Semi-supervised multi-instance multi-label （SSMIML） learning framework, which aims at taking full advantage of both labeled and unlabeled data to address the annotation problem. Specifically, a reinforced diverse density algorithm is applied firstly to select the Instance prototypes （IPs） with respect to a given keyword from both positive and unlabeled bags. Then, the selected IPs are modeled using the Gaussian mixture model （GMM） in order to reflect the semantic class density distribution. Furthermore, based on the class distribution for a keyword, both positive and unlabeled bags are redefined using a novel feature mapping strategy. Thus, each bag can be represented by one fixed-length feature vector so that the manifold-ranking algorithm can be used subsequently to propagate the corresponding label from positive bags to unlabeled bags directly. Experiments on the Corel data set show that the proposed method outperforms most existing image annotation algorithms.     

Automatic Region-Based Image Annotation Using an Improved Multiple-Instance Learning Algorithm

Many existing image annotation algorithms work under probabilistic modeling mechanism. In this paper, we formulate the problem as a variation of supervised learning task and propose an Improved Citation kNN （ICKNN） Multiple-instance learning （MIL） algorithm for automatic image annotation. In contrast with the existing MIL based image annotation algorithm which intends to learn an explicit correspondence between image regions and keywords, here we annotate the keywords on the entire image instead of its regions. Concretely, we first explore the concept of Confidence weight （CW） for every training bag （image） to reflect the relevance extent between a bag and a semantic keyword. It can be treated as a stage of re-ranking on training set before an- notation starts. Moreover, a modified hausdoriT distance is adopted for the ICKNN algorithm to solve the automatic annotation problem. The proposed annotation approach demonstrates a promising performance over 5,000 images from COREL dataset, as compared with some current algorithms in the literature.     

面向帕金森病语音诊断的非监督两步式卷积稀疏迁移学习算法

帕金森病(Parkinson's Disease,PD)语音诊断存在小样本问题,如果借助相关语音数据集进行迁移学习,容易加重训练集和测试集之间的分布差异,影响分类准确率.为了解决上述矛盾问题,本文提出了两步式稀疏迁移学习算法.该算法分为两大步:第一步算法为语音段特征同时优选的快速卷积稀疏编码算法,构造卷积稀疏编码算子用...     

Diagnostic Classification of Lung Cancer Using Deep Transfer Learning Technology and Multi-Omics Data

In recent years,with the increasing application of highthroughput sequencing technology,researchers have obtained and accumulated a large amount of multi-omics ...     

Classification of Breast Abnormalities Using a Deep Convolutional Neural Network and Transfer Learning

Journal of Communications Technology and Electronics - A new algorithm for classification of breast pathologies in digital mammography using a convolutional neural network and transfer learning is...     

An Algorithm for Idle-State Detection and Continuous Classifier Design in Motor-Imagery-Based BCI

Abstract-The development of asynchronous brain-computer interface （BCI） based on motor imagery （M1） poses the research in algorithms for detecting the nontask states （i.e., idle state） and the design of continuous classifiers that classify continuously incoming electroencephalogram （EEG） samples. An algorithm is proposed in this paper which integrates two two-class classifiers to detect idle state and utilizes a sliding window to achieve continuous outputs. The common spatial pattern （CSP） algorithm is used to extract features of EEG signals and the linear support vector machine （SVM） is utilized to serve as classifier. The algorithm is applied on dataset IVb of BCI competition Ⅲ, with a resulting mean square error of 0.66. The result indicates that the proposed algorithm is feasible in the first step of the development of asynchronous systems.     

A User-Friendly SSVEP-Based BCI Using Imperceptible Phase-Coded Flickers at 60Hz

A brain-computer interface(BCI) system based on steady-state visual evoked potentials(SSVEP) was developed by four-class phase-coded stimuli. SSVEPs elicited by flickers at 60 Hz, which is higher than the critical fusion frequency(CFF),were compared with those at 15 Hz and 30 Hz. SSVEP components in electroencephalogram(EEG) were detected using task related component analysis(TRCA)method. Offline analysis with 17 subjects indicated that the highest information transfer rate(ITR) was29.80±4.65 bp...     

Cogent Machine Learning Algorithm for Indoor and Underwater Localization Using Visible Light Spectrum

Wireless Personal Communications - From last few years indoor localization has become more popular for wireless devices. The major reason for its popularity is to access current location...     

Green Computing Process and its Optimization Using Machine Learning Algorithm in Healthcare Sector

Mobile Networks and Applications - Handling the information is crucial task in healthcare sector; the data mining techniques will be right choice to address the complex problems. The hybridized...     

ERIL: An Algorithm for Emotion Recognition from Indian Languages Using Machine Learning

Wireless Personal Communications - It is critical for a computer to understand the speaker’s mood during a human–machine conversation. Until now, we’ve only used neutral phrases...     

基于自适应投影学习算法的RBF神经网络多用户检测方法

自适应投影学习算法是一种简单有效的构造和训练径向基函数神经网络的方法,该方法能迭代地确定径向基函数的个数,中心的位置以及网络的权系数。本文将基于自适应投影学习算法的径向基函数神经网络应用于CDMA系统多用户检测,仿真表明:这种方法对远近问题不敏感,具有良好的误码率性能和抗多址干扰性能。     

Autonomous Leukemia Detection Scheme Based on Hybrid Convolutional Neural Network Model Using Learning Algorithm

Wireless Personal Communications - Unmanned Aerial Vehicles (UAVs) have recently attracted attention in military areas as well as a wide range of commercial and civilian applications. With UAVs...     

BCI competition III: dataset II- ensemble of SVMs for BCI P300 speller

Brain-computer interface P300 speller aims at helping patients unable to activate muscles to spell words by means of their brain signal activities. Associated to this BCI paradigm, there is the problem of classifying electroencephalogram signals related to responses to some visual stimuli. This paper addresses the problem of signal responses variability within a single subject in such brain-computer interface. We propose a method that copes with such variabilities through an ensemble of classifiers approach. Each classifier is composed of a linear support vector machine trained on a small part of the available data and for which a channel selection procedure has been performed. Performances of our algorithm have been evaluated on dataset II of the BCI Competition III and has yielded the best performance of the competition.     

一种量子竞争学习算法

量子计算（Quantum Computation）以其独特的性能引起广泛瞩目。本文尝试将量子计算与传统的神经计算结合起来,通过设计若干个量子算子来构造Hamming神经网络的量子对照物,从而提出一种量子竞争学习算法（Quantum Competitive Learning Algorithm,QCLA）,它能够实现模式分类和联想记忆。     

BP网络局部学习速率自适应SA算法的改进

Silva-Almeida(SA)算法是最好的局部学习速率自适应算法之一,在对SA算法进行研究分析的基础上,提出 两项改进措施,使改进后的SA算法较原SA算法震荡现象大大减弱,训练速率有较大加快,训练精度有较大提高。在仿 真实验中,改进的SA算法在一定程度上优于RPROP算法。     

Hybrid Global Sensitivity Analysis Based Optimal Attribute Selection Using Classification Techniques by Machine Learning Algorithm

In this study, we focus on realizing channel estimation using a fully connected deep neural network. The data aided estimation approach is employed. We assume the transmission channel is Rayleigh and it is constant over the duration of a symbol plus pilot transmission. We develop and tune the deep learning model for various size of pilot data that is known to the receiver and used for channel estimation. The deep learning models are trained on the Rayleigh channel. The performance of the model is discussed for various size of pilot by providing Bit Error Rate of the model. The Bit Error Rate performance of the model is compared to theoretical upper bound which shows that the model successfully estimates the channel.

     

神经网比例式学习算法

本文提出一种适用于训练大型BP神经网的学习算法,即比例式学习算法。通过理论分析和计算机模拟结果表明,比例式学习算法学习速度快,是一种可行的学习算法。