2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)最新文献_第4页

Path for Kernel Adaptive One-Class Support Vector Machine 核自适应一类支持向量机路径

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.127

Van Khoa Le, P. Beauseroy

引用次数: 2

Measuring Level-K Reasoning, Satisficing, and Human Error in Game-Play Data 衡量游戏玩法数据中的k级推理、满意度和人为错误

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.233

Tamal Biswas, Kenneth W. Regan

{"title":"Measuring Level-K Reasoning, Satisficing, and Human Error in Game-Play Data","authors":"Tamal Biswas, Kenneth W. Regan","doi":"10.1109/ICMLA.2015.233","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.233","url":null,"abstract":"Inferences about structured patterns in human decision making have been drawn from medium-scale simulated competitions with human subjects. The concepts analyzed in these studies include level-k thinking, satisficing, and other human error tendencies. These concepts can be mapped via a natural depth of search metric into the domain of chess, where copious data is available from hundreds of thousands of games by players of a wide range of precisely known skill levels in real competitions. The games are analyzed by strong chess programs to produce authoritative utility values for move decision options by progressive deepening of search. Our experiments show a significant relationship between the formulations of level-k thinking and the skill level of players. Notably, the players are distinguished solely on moves where they erred -- according to the average depth level at which their errors are exposed by the authoritative analysis. Our results also indicate that the decisions are often independent of tail assumptions on higher-order beliefs. Further, we observe changes in this relationship in different contexts, such as minimal versus acute time pressure. We try to relate satisficing to insufficient level of reasoning and answer numerically the question, why do humans blunder?","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122484373","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: 12

Multi-level Resolution Features for Classification of Transportation Trajectories 运输轨迹分类的多级分辨率特征

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.66

Aidan Macdonald, Jeffrey S. Ellen

引用次数: 0

An Automatic Recognition for the Auditory Brainstem Response Waveform 听觉脑干反应波形的自动识别

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.92

Balemir Uragun

引用次数: 3

A Hierarchical Deep Neural Network for Fault Diagnosis on Tennessee-Eastman Process 基于层次深度神经网络的Tennessee-Eastman过程故障诊断

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.208

Danfeng Xie, Limei Bai

引用次数: 39

Coordinate Descent Fuzzy Twin Support Vector Machine for Classification 坐标下降模糊双支持向量机分类

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.35

Bin-Bin Gao, Jianjun Wang, Yao Wang, Chan-Yun Yang

引用次数: 26

A Demonstration of Stability-Plasticity Imbalance in Multi-agent, Decomposition-Based Learning 基于分解的多智能体学习中稳定性-可塑性不平衡的论证

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.106

Sean C. Mondesire, R. P. Wiegand

{"title":"A Demonstration of Stability-Plasticity Imbalance in Multi-agent, Decomposition-Based Learning","authors":"Sean C. Mondesire, R. P. Wiegand","doi":"10.1109/ICMLA.2015.106","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.106","url":null,"abstract":"Layered learning is a machine learning paradigm used in conjunction with direct-policy search reinforcement learning methods to find high performance agent behaviors for complex tasks. At its core, layered learning is a decomposition-based paradigm that shares many characteristics with robot shaping, transfer learning, hierarchical decomposition, and incremental learning. Previous studies have provided evidence that layered learning has the ability to outperform standard monolithic methods of learning in many cases. The dilemma of balancing stability and plasticity is a common problem in machine learning that causes learning agents to compromise between retaining learned information to perform a task with new incoming information. Although existing work implies that there is a stability-plasticity imbalance that greatly limits layered learning agents' ability to learn optimally, no work explicitly verifies the existence of the imbalance or its causes. This work investigates the stability-plasticity imbalance and demonstrates that indeed, layered learning heavily favors plasticity, which can cause learned subtask proficiency to be lost when new tasks are learned. We conclude by identifying potential causes of the imbalance in layered learning and provide high level advice about how to mitigate the imbalance's negative effects.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115551647","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

Probabilistic Models for One-Day Ahead Solar Irradiance Forecasting in Renewable Energy Applications 可再生能源应用中一天前太阳辐照度预测的概率模型

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.137

C. V. A. Silva, L. Lim, D. Stevens, D. Nakafuji

引用次数: 9

Superposed Naive Bayes for Accurate and Interpretable Prediction 用于精确和可解释预测的叠加朴素贝叶斯

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.147

Toshiki Mori

{"title":"Superposed Naive Bayes for Accurate and Interpretable Prediction","authors":"Toshiki Mori","doi":"10.1109/ICMLA.2015.147","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.147","url":null,"abstract":"Background: Data mining and machine learning techniques have been widely applied in software engineering research. However, past research has mainly focused on only prediction accuracy. Aim: The interpretability of prediction results should be accorded greater emphasis in software engineering research. A prediction model that has high accuracy and explanatory power is required. Method: We propose a new algorithm of naïve Bayes ensemble, called superposed naïve Bayes (SNB), which firstly builds an ensemble model with high prediction accuracy and then transforms it into an interpretable naïve Bayes model. Results: We conducted an experiment with the NASA MDP datasets, in which the performance and interpretability of the proposed method were compared with those of other classification techniques. The results of the experiment indicate that the proposed method can produce balanced outputs that satisfy both performance and interpretability criteria. Conclusion: We confirmed the effectiveness of the proposed method in an experiment using software defect data. The model can be extensively applied to other application areas, where both performance and interpretability are required.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121477098","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

Extracting Topical Information of Tweets Using Hashtags 利用标签提取推文的主题信息

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.73

Z. Alp, Ş. Öğüdücü

引用次数: 14