International Conference on Machine Learning and Soft Computing最新文献_第3页

Evaluation of open information extraction methods using Reuters-21578 database 基于Reuters-21578数据库的公开信息提取方法评价

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184099

J. M. Rodríguez, H. Merlino, Patricia Pesado, Ramón García-Martínez

引用次数: 2

Machine learning based evaluation of functional index for coronary lesion severity 基于机器学习的冠状动脉病变严重程度功能指标评价

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184079

Due Minh Tran, M. Nguyen, Sang-Wook Lee

引用次数: 4

Face tracking with convolutional neural network heat-map 基于卷积神经网络热图的人脸跟踪

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184081

Nhu-Tai Do, Soohyung Kim, Hyung-Jeong Yang, Gueesang Lee, In Seop Na

引用次数: 7

Student proficiency prediction on CNMOOC data 基于CNMOOC数据的学生熟练程度预测

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184098

Qi Wang, Liping Shen

引用次数: 2

Classification of sentiments in short-text: an approach using mSMTP measure 短文本情感分类:一种使用mSMTP度量的方法

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184074

H. Kumar, B. Harish, S. V. A. Kumar, Manjunath Aradhya

{"title":"Classification of sentiments in short-text: an approach using mSMTP measure","authors":"H. Kumar, B. Harish, S. V. A. Kumar, Manjunath Aradhya","doi":"10.1145/3184066.3184074","DOIUrl":"https://doi.org/10.1145/3184066.3184074","url":null,"abstract":"Sentiment analysis or opinion mining is an automated process to recognize opinion, moods, emotions, attitude of individuals or communities through natural language processing, text analysis, and computational linguistics. In recent years, many studies concentrated on numerous blogs, tweets, forums and consumer review websites to identify sentiment of the communities. The information retrieved from social networking site will be in short informal text because of limited characters in blogging site or consumer review websites. Sentiment analysis in short-text is a challenging task, due to limitation of characters, user tends to shorten his/her conversation, which leads to misspellings, slang terms and shortened forms of words. Moreover, short-texts consists of more number of presence and absence of term/feature compared to regular text. In this work, our major goal is to classify sentiments into positive, negative or neutral polarity using new similarity measure. The proposed method embeds modified Similarity Measure for Text Processing (mSMTP) with K-Nearest Neighbor (KNN) classifier. The effectiveness of the proposed method is evaluated by comparing with Euclidean Distance, Cosine Similarity, Jaccard Coefficient and Correlation Coefficient. The proposed method is also compared with other classifiers like Support Vector Machine and Random Forest using benchmark dataset. The classification results are evaluated based on Accuracy, Precision, Recall and F-measure.","PeriodicalId":109559,"journal":{"name":"International Conference on Machine Learning and Soft Computing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115528124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

An anti-phishing method based on feature analysis 一种基于特征分析的反网络钓鱼方法

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184082

M. Rajab

引用次数: 7

Face image generation system using attribute information with DCGANs 人脸图像生成系统利用属性信息与dcgan

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184071

Yurika Sagawa, M. Hagiwara

{"title":"Face image generation system using attribute information with DCGANs","authors":"Yurika Sagawa, M. Hagiwara","doi":"10.1145/3184066.3184071","DOIUrl":"https://doi.org/10.1145/3184066.3184071","url":null,"abstract":"In this paper, we propose an attribute added face image generation system using Deep Convolutional Generative Adversarial Networks(DCGANs). Convolutional Neural Networks(CNNs) can extract important features of an image and attain high precision in image classification tasks. In the proposed system, image features are extracted using CNNs, and attribute features added to image features, and attributes added images are generated by DCGANs. Specifically, we use the attributes of \"smile\" and \"male\", and work on a task of generating smile images from non-smile images, and a task of generating male images from female images. Since the training of the proposed system requires image pairs including with and without attributes, we use two extraction methods, 1)Usage of attribute label attached dataset, 2)Usage of cosine similarity. To obtain attribute features, we trained 4-layer CNNs which are the same architecture as Discriminator of GANs, to classify images into two classes, with and without attributes. Here, attribute features are defined as the averaged difference between image features with and without attributes, more specifically, the values in the final convolution layer in the 4-layer CNNs. We performed two kinds of evaluation experiments: the first one is a subjective evaluation experiment on items such as \"whether generated images have attributes\", the second one is a quantitative evaluation experiment for measuring whether the people shown in the input image and the generated image are the same person. As the results, excellent characteristics were obtained.","PeriodicalId":109559,"journal":{"name":"International Conference on Machine Learning and Soft Computing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129591687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Solving document clustering problem through meta heuristic algorithm: black hole 用元启发式算法解决文档聚类问题:黑洞

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184085

Muhammad Rafi, Bilal Aamer, Mubashir Naseem, M. Osama

{"title":"Solving document clustering problem through meta heuristic algorithm: black hole","authors":"Muhammad Rafi, Bilal Aamer, Mubashir Naseem, M. Osama","doi":"10.1145/3184066.3184085","DOIUrl":"https://doi.org/10.1145/3184066.3184085","url":null,"abstract":"The paper proposed a soft computing approach to solve document clustering problem. Document clustering is a specialized clustering problem in which textual documents autonomously segregated to a number of identifiable, subject homogenous and smaller sub-collections (also called clusters). Identifying implicit textual patterns within the documents is a challenging aspect as there can be thousands of such textual features. Partition clustering algorithm like k-means is mainly used for this problem. There are several drawbacks in k-means algorithm such as (i) initial seeds dependency, and (ii) it traps into local optimal solution. Although every k-means solution may contain some good partial arrangements for clustering. Meta-heuristic algorithm like Black Hole (BH) uses certain trade-off of randomization and local search for finding the optimal and near optimal solution. Our motivation comes from the fact that meta-heuristic optimization can quickly produce a global optimal solution using random k-means initial solution. The contributions from this research are (i) an implementation of black hole algorithm using k-mean as embedding (ii) The phenomena of global search and local search optimization are used as parameters adjustments. A series of experiments are performed with our proposed method on standard text mining datasetslike: (i) NEWS20, (ii) Reuters and (iii) WebKB and results are evaluated on Purity and Silhouette Index. In comparison the proposed method outperforms the basic k-means, GA with k-means embedding and quickly converges to global or near global optimal solution.","PeriodicalId":109559,"journal":{"name":"International Conference on Machine Learning and Soft Computing","volume":"261 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133652270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

EM based machine learning attack for XOR arbiter PUF 基于EM的XOR仲裁器PUF机器学习攻击

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184100

Y. Nozaki, M. Yoshikawa

引用次数: 2

Implementation of adversarial scenario to malware analytic 实现对抗性场景的恶意软件分析

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184078

Chia-Min Lai, Chia-Yu Lu, Hahn-Ming Lee

引用次数: 0