2016 5th Brazilian Conference on Intelligent Systems (BRACIS)最新文献_第6页

Finding Inference Rules Using Graph Mining in Ontological Knowledge Bases 利用图挖掘在本体知识库中寻找推理规则

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.070

L. Navarro, Estevam Hruschka, A. P. Appel

引用次数: 1

Pixel Clustering for Face Recognition 人脸识别中的像素聚类

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.032

Tiago Buarque Assunção de Carvalho, M. Sibaldo, Ing Ren Tsang, George D. C. Cavalcanti, I. Tsang, Jan Sijbers

{"title":"Pixel Clustering for Face Recognition","authors":"Tiago Buarque Assunção de Carvalho, M. Sibaldo, Ing Ren Tsang, George D. C. Cavalcanti, I. Tsang, Jan Sijbers","doi":"10.1109/BRACIS.2016.032","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.032","url":null,"abstract":"This work proposes a theoretical framework for an unsupervised feature extraction called Pixel Clustering. The main idea is based on the clustering of the pixels in order to mitigate the multicollinearity issue and a new feature is extracted for each cluster of similar pixels. This allows to define feature extraction techniques by setting just three parts: (1) defining pixel vectors in the training set, each pixel vector is a representative for a pixel on every training image, (2) a clustering algorithm for the pixels vectors, (3) finally it is performed a linear combination of the pixel into a cluster, in order to create a single feature per cluster. The framework also makes it possible to create simpler, computationally cheaper and more general new implementations of well known feature extraction methods such as Waveletfaces. Two extraction methods are implemented and tested in three face datasets. Test results are compared to the traditional Eigenfaces and others state-of-art feature extraction methods for face recognition. The proposed method achieves up to 38% higher face recognition rate than Eigenfaces, if few classes are used for training the projections.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127194883","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: 6

Exploring Unsupervised Features in Conditional Random Fields for Spanish Named Entity Recognition 探索西班牙语命名实体识别中条件随机场的无监督特征

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.059

J. Copara, J. Ochoa, Camilo Thorne, Goran Glavas

引用次数: 4

(Deep) Learning from Frames (深度)框架学习

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.012

Jonatas Wehrmann, Rodrigo C. Barros, Gabriel S. Simões, Thomas S. Paula, D. Ruiz

{"title":"(Deep) Learning from Frames","authors":"Jonatas Wehrmann, Rodrigo C. Barros, Gabriel S. Simões, Thomas S. Paula, D. Ruiz","doi":"10.1109/BRACIS.2016.012","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.012","url":null,"abstract":"Learning content from videos is not an easy task and traditional machine learning approaches for computer vision have difficulties in doing it satisfactorily. However, in the past couple of years the machine learning community has seen the rise of deep learning methods that significantly improve the accuracy of several computer vision applications, e.g., Convolutional Neural Networks (ConvNets). In this paper, we explore the suitability of ConvNets for the movie trailers genre classification problem. Assigning genres to movies is particularly challenging because genre is an immaterial feature that is not physically present in a movie frame, so off-the-shelf image detection models cannot be directly applied to this context. Hence, we propose a novel classification method that encapsulates multiple distinct ConvNets to perform genre classification, namely CoNNECT, where each ConvNet learns features that capture distinct aspects from the movie frames. We compare our novel approach with the current state-of-the-art techniques for movie classification, which make use of well-known image descriptors and low-level handcrafted features. Results show that CoNNECT significantly outperforms the state-of-the-art approaches in this task, moving towards effectively solving the genre classification problem.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"11 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114052130","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: 20

The Role of Discount Factor in Risk Sensitive Markov Decision Processes 折现因子在风险敏感马尔可夫决策过程中的作用

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.092

Valdinei Freire

引用次数: 4

Extreme Risk Averse Policy for Goal-Directed Risk-Sensitive Markov Decision Process 目标导向风险敏感马尔可夫决策过程的极端风险厌恶策略

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.025

Valdinei Freire, K. V. Delgado

引用次数: 2

Knowledge Representation for Argumentation in Agent-Oriented Programming Languages 面向智能体编程语言中论证的知识表示

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.014

Alison R. Panisson, Rafael Heitor Bordini

{"title":"Knowledge Representation for Argumentation in Agent-Oriented Programming Languages","authors":"Alison R. Panisson, Rafael Heitor Bordini","doi":"10.1109/BRACIS.2016.014","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.014","url":null,"abstract":"Argumentation in multi-agent systems provides both a mechanism for agent reasoning under uncertainty and conflicting information as well as for communication in a more elaborate way, allowing agents to understand each other through the exchange of additional information when compared to other forms of agent communication. Even though argumentation techniques can play an important role in multi-agent systems, little research has been carried out on the issues in integrating argumentation techniques and agent-oriented programming languages, which would allow the development of practical applications taking advantage of such combined techniques. In this work, we present an argumentation framework developed on the basis of an agent-oriented programming language. We cover mainly the practical aspects of such integration, focusing on the knowledge representation expressivity resulting from it. Our approach allows the development of multi-agent applications where agents are able to use arguments in their decision-making processes as well as for communication. The framework has been successfully used as part of the development of a healthcare multi-agent prototype application.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121967417","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: 19

Shrinkage k-Means: A Clustering Algorithm Based on the James-Stein Estimator 收缩k均值:一种基于James-Stein估计的聚类算法

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.084

Filipe F. R. Damasceno, Marcelo B. A. Veras, D. Mesquita, J. Gomes, Carlos Brito

引用次数: 4

A Pareto-Based Symbiotic Relationships Model for Unconstrained Continuous Optimization 基于pareto的无约束连续优化共生关系模型

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.066

Leanderson André, R. S. Parpinelli

{"title":"A Pareto-Based Symbiotic Relationships Model for Unconstrained Continuous Optimization","authors":"Leanderson André, R. S. Parpinelli","doi":"10.1109/BRACIS.2016.066","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.066","url":null,"abstract":"Symbiotic relationships are one of several phenomena that can be observed in nature. These relationships consist of interactions between organisms and can lead to benefits or damages to those involved. In an optimization context, symbiotic relationships can be used to perform information exchange between populations of candidate solutions to a given problem. This paper presents an information exchange model inspired by symbiotic relationships and applies the model to unconstrained single-objective continuous optimization problems. The symbiotic relationships are modelled using the Pareto dominance criteria inside a computational ecosystem for optimization. The Artificial Bee Colony algorithm is used to compound the populations of the ecosystem. Four models of relationships are analyzed: slavery, competition, altruism and mutualism. Thirty unconstrained single-objective continuous benchmark functions with high number of dimensions (d = 200) are tested and obtained results compared. Results suggest that the proposed model for information exchange favors the balance between exploration and exploitation leading to better results.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129410727","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: 0

Building Ensembles with Classifier Selection Using Self-Organizing Maps 使用自组织映射构建具有分类器选择的集成

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.087

L. Almeida, C. Zanchettin, Hilton Pintor Bezerra Leite

引用次数: 0