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检索条件"任意字段=1st International Workshop on Machine Learning and Data Mining in Pattern Recognition"

共 585 条记录，以下是261-270 订阅

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1st international workshop on Personal Analytics and Privacy, PAP 2017, Held in Conjunction with the European Conference on machine learning and Principles and Practice of Knowledge Discovery in databases, ECML PKDD 2017

1st International Workshop on Personal Analytics and Privacy...

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1st international workshop on Personal Analytics and Privacy, PAP 2017, Held in Conjunction with the European Conference on machine learning and Principles and Practice of Knowledge Discovery in databases, ECML PKDD 2017

ISBN: (纸本)9783319719696

The proceedings contain 13 papers. The special focus in this conference is on Personal Analytics and Privacy. The topics include: Churn prediction using dynamic RFM-augmented Node2vec;multi-scale community detection in temporal networks using spectral graph wavelets;influence maximization-based event organization on social networks;from self-data to self-preferences: Towards preference elicitation in personal information management systems;assessing privacy risk in retail data;differential privacy and neural networks: A preliminary analysis;co-clustering for differentially private synthetic data generation;evaluating the impact of friends in predicting user’s availability in online social networks;movement behaviour recognition for water activities;automatic recognition of public transport trips from mobile device sensor data and transport infrastructure information;Guess the movie - Linking facebook pages to IMDb movies.

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On Using Pretext Tasks to Learn Representations from Network Logs 1

On Using Pretext Tasks to Learn Representations from Network...

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1st ACM international workshop on Native Network Intelligence (NativeNI) Part of 18th international Conference on Emerging Networking Experiments and Technologies (CoNEXT)

作者： Boffa, Matteo Vassio, Luca Mellia, Marco Drago, Idilio Milan, Giulia Ben Houidi, Zied Rossi, Dario Politecn Torino Italy Turin Italy Univ Torino Turin Italy Huawei Technol Co Ltd Paris France

ISBN: (纸本)9781450398879

learning meaningful representations from network data is critical to ease the adoption of AI as a cornerstone to process network logs. Since a large portion of such data is textual, Natural Language Processing (NLP) appears as an obvious candidate to learn their representations. Indeed, the literature proposes impressive applications of NLP applied to textual network data. However, in the absence of labels, objectively evaluating the goodness of the learned representations is still an open problem. We call for a systematic adoption of domain-specific pretext tasks to select the best representation from network data. Relying on such tasks enables us to evaluate different representations on side machine learning problems and, ultimately, unveiling the best candidate representations for the more interesting downstream tasks for which labels are scarce or unavailable. We apply pretext tasks in the analysis of logs collected from SSH honeypots. Here, a cumbersome downstream task is to cluster events that exhibit a similar attack pattern. We propose the following pipeline: first, we represent the input data using a classic NLP-based approach. Then, we design pretext tasks to objectively evaluate the representation goodness and to select the best one. Finally, we use the best representation to solve the unsupervised task, which uncovers interesting behaviours and attack patterns. All in all, our proposal can be generalized to other text-based network logs beyond honeypots.

关键词： Representation learning Cybersecurity NLP Honeypots

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Multi-task Regularization of Generative Similarity Models

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1st international workshop on Similarity-Based pattern recognition (SIMBAD)

作者： Cazzanti, Luca Feldman, Sergey Gupta, Maya R. Gabbay, Michael Univ Washington Appl Phys Lab Seattle WA 98105 USA Univ Washington Dept Elect Engn Seattle WA 98105 USA

ISBN: (纸本)9783642244704

We investigate a multi-task approach to similarity discriminant analysis, where we propose treating the estimation of the different class-conditional distributions of the pairwise similarities as multiple tasks. We show that regularizing these estimates together using a least-squares regularization weighted by a task-relatedness matrix can reduce the resulting maximum a posteriori classification errors. Results are given for benchmark data sets spanning a range of applications. In addition, we present a new application of similarity-based learning to analyzing the rhetoric of multiple insurgent groups in Iraq. We show how to produce the necessary task relatedness information from standard given training data, as well as how to derive task-relatedness information if given side information about the class relatedness.

关键词： similarity generative similarity-based classification discriminant analysis multi-task learning regularization

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mining Transcriptional Association Rules from Breast Cancer Profile data

Mining Transcriptional Association Rules from Breast Cancer ...

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12th IEEE international Conference on Information Reuse and Integration (IRI)/1st international workshop on Issues and Challenges in Social Computing (WICSOC)

作者： Malpani, Rakhi Lu, Meiliu Zhang, Du Sung, Wing Kin Calif State Univ Sacramento Dept Comp Sci Sacramento CA 95819 USA Natl Univ Singapore Sch Comp Singapore 117548 Singapore Genome Inst Singapore Singapore 117548 Singapore

ISBN: (纸本)9781457709661

To gain insight into regulatory mechanisms underlying the transcription process of gene expressions, we need to understand the co-expressed gene sets under common regulatory mechanisms. Though computational methods have been developing to identify expression module, challenges still remain for cancer related gene expression profiling. In this paper, we have developed a method of data preprocessing and two different association rule mining approaches for discovering breast cancer regulatory mechanisms of gene module. Our data preprocessing task involved with two independent data sources: (a) a single breast cancer patient profile data file, (b) a candidate enhancer information data file. Using the integrated data, we also conducted four experiments of the association rule mining.

关键词： data mining Information Integration data preprocessing Association rule mining machine learning

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Predicting Song Popularity Through machine learning and Sentiment Analysis on Social Networks 13th

Predicting Song Popularity Through Machine Learning and Sen...

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13th mining Humanistic data workshop, MHDW 2024, 9th workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 international workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Rompolas, Gerasimos Smpoukis, Athanasios Kafeza, Eleanna Makris, Christos Computer Engineering and Informatics Department University of Patras Patras26504 Greece College of Interdisciplinary Studies Zayed University Dubai144534 United Arab Emirates

ISBN: (纸本)9783031632266

In this paper, we delve into the intricate relationship between technology, music, and success. Our research focuses on leveraging the capabilities of machine learning algorithms to forecast the success and popularity of songs. By combining audio features, social media data, and emotion analysis, our machine learning-based model demonstrates its capability as a reliable tool for artists, amateurs, and music industry stakeholders to make informed and targeted decisions. Our analysis encompassed not only the inherent audio characteristics of each track but also the extent of audience engagement and emotional response of the fans. Through the integration of textual emotion analysis, we quantified the reactions of the audience and performed an emotion labelling, leveraging our social information to uncover the emotions surrounding each track’s release. The experimental results show that the feature data from which the classification algorithms were trained, unfolded to be qualitative and precise, while they also show that the use of the social media features improves the classifiers’ performance in predicting the popularity of the tracks. © IFIP international Federation for Information Processing 2024.

关键词： Emotion recognition

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Facing Digital Agriculture Challenges with Knowledge Engineering 1

Facing Digital Agriculture Challenges with Knowledge Enginee...

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1st IEEE international Conference on Artificial Intelligence for Industries (AI4I)

作者： Nery, Marcelo Santos, Rodrigo Santos, Wallas Lourenco, Vitor Moreno, Marcio IBM Res Rio De Janeiro Brazil

ISBN: (纸本)9781538692097

Knowledge Engineering is key to enable knowledge extraction, representation and reasoning, leading to better business insights and decisions. Current advances in machine learning and new trends in AI are bringing a plethora of algorithms capable of performing advanced pattern recognition and data classification. The ability to link, to organize and to query the outputs of these algorithms as well as the ability to handle huge amounts of data and its multiple sources is crucial to maximize the potential of such advances, specially over large datasets. This paper presents challenges in the context of digital agriculture and our position in moving forward with these capabilities whilst using knowledge engineering techniques.

关键词： Knowledge Engineering machine learning Hybrid knowledge representation Digital agriculture

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A study on possibilistic and fuzzy possibilistic C-means clustering algorithms for data clustering 1

A study on possibilistic and fuzzy possibilistic C-means clu...

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1st international Conference on Emerging Trends in Science, Engineering and Technology, INCOSET 2012

作者： Mohamed Jafar, O.A. Sivakumar, R. Tiruchirappalli India Poondi Thanjavur India

ISBN: (纸本)9781467351447

data clustering is one of the important data mining tasks. It is the process of grouping objects into clusters such that objects in the same clusters are more similar to each other than the objects in different clusters. It has been applied in many fields, including data mining, data analysis, document retrieval, machine learning, pattern recognition, bioinformatics and image analysis. Fuzzy c-means (FCM) algorithm is one of the important clustering techniques. However, it is sensitive to noises and is easily struck at local minima. The possibilistic c-means (PCM) algorithm proposed in the literature solves the noise sensitivity problem of FCM algorithm. However, the performance of PCM depends heavily on the initialization and often deteriorates due to the coincident clustering problem. Fuzzy possibilistic c-means algorithm (FPCM) solves the problems of both FCM and PCM algorithms. In this paper, we studied PCM and FPCM clustering techniques. The algorithms are tested with five real world data sets and randomly generated data set. A brief review of applications of these algorithms is also described. © 2012 IEEE.

关键词： data mining

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Lateral Aerodynamic Parameters Estimation using Neuro Artificial Bee Colony Fusion Algorithm (NABC) 1

Lateral Aerodynamic Parameters Estimation using Neuro Artifi...

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1st international Conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

作者： Kumar, Prashant Sonkar, Sarvesh Ghosh, Ajoy Kanti Philip, Deepu Aerospace Engineering Indian Institute of Technology Kanpur India Design Programme Indian Institute of Technology Kanpur India Industrialand Management Engineering Indian Institute of Technology Kanpur Kanpur India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Aerodynamic parameter estimation entails modelling force and moment coefficients as well as computing stability and control derivatives from flight data. This topic has been thoroughly researched utilizing traditional procedures such as output, filter, and equation error methods. machine learning, such as artificial neural networks, provides an alternate way to these model-based methodologies. This paper proposes a novel estimation technique for aerodynamic parameters of a real aircraft in the presence of system and measurement uncertainty. A fusion between biologically inspired optimization i.e., Artificial Bee Colony (ABC) optimization and widely used Artificial Neural Network (ANN), which mimics the functional unit of the brain, the neuron, has been demonstrated to be novel and a promising method to the challenges of system identification and parameter estimation (sensor noise). The obtained results were compared to Least Square, and Maximum Likelihood Method (MLE), benchmark estimation techniques. © 2022 IEEE.

关键词： machine learning

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3rd international workshop on Explainable Artificial Intelligence in Healthcare, XAI-Healthcare 2023 and 1st international workshop on Process mining Applications for Healthcare, PM4H 2023

3rd International Workshop on Explainable Artificial Intelli...

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3rd international workshop on Explainable Artificial Intelligence in Healthcare, XAI-Healthcare 2023 and 1st international workshop on Process mining Applications for Healthcare, PM4H 2023

ISBN: (纸本)9783031543029

The proceedings contain 12 papers. The special focus in this conference is on Explainable Artificial Intelligence in Healthcare. The topics include: Interpreting machine learning Models for Survival Analysis: A study of Cutaneous Melanoma Using the SEER database;probExplainer: A Library for Unified Explainability of Probabilistic Models and an Application in Interneuron Classification;An Explainable AI Framework for Treatment Failure Model for Oncology Patients;explanations of Symbolic Reasoning to Effect Patient Persuasion and Education;explainable Artificial Intelligence in Response to the Failures of Musculoskeletal Disorder Rehabilitation;phenotypes vs Processes: Understanding the Progression of Complications in Type 2 Diabetes. A Case study;A data-Driven Framework for Improving Clinical Managements of Severe Paralytic Ileus in ICU: From Path Discovery, Model Generation to Validation;preface;understanding Prostate Cancer Care Process Using Process mining: A Case study;From Script to Application. A bupaR Integration into PMApp for Interactive Process mining Research.

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Combining independent component analysis with support vector machines

Combining independent component analysis with support vector...

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1st international Symposium on Systems and Control in Aerospace and Astronautics

作者： Genting, Yan Guangfu, Ma Jianting, Lv Bin, Song Department of Control Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)0780393953

Recently, support vector machine (SVM) has become a popular tool in pattern recognition. In developing a successful SVM classifier, the first step is feature extraction. This paper proposes the application of independent component analysis (ICA) to SVM for feature extraction. In ICA, the original inputs are linearly transformed into features which are mutually statistically independent. By examining the statlog heart disease data and satimage data, the experimental shows that SVM by feature extraction using ICA can perform better than that without feature extraction.

关键词： learning systems

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