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On the Usefulness of Similarity Based Projection Spaces for Transfer learning

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

作者： Morvant, Emilie Habrard, Amaury Ayache, stephane Aix Marseille Univ Lab Informat Fondamentale Marseille CNRS UMR 6166 F-13453 Marseille 13 France

ISBN: (纸本)9783642244704;9783642244711

Similarity functions are widely used in many machine learning or pattern recognition tasks. We consider here a recent framework for binary classification, proposed by Balcan et al., allowing to learn in a potentially non geometrical space based on good similarity functions. This framework is a generalization of the notion of kernels used in support vector machines in the sense that allows one to use similarity functions that do not need to be positive semi-definite nor symmetric. The similarities are then used to define an explicit projection space where a linear classifier with good generalization properties can be learned. In this paper, we propose to study experimentally the usefulness of similarity based projection spaces for transfer learning issues. More precisely, we consider the problem of domain adaptation where the distributions generating learning data and test data are somewhat different. We stand in the case where no information on the test labels is available. We show that a simple renormalization of a good similarity function taking into account the test data allows us to learn classifiers more performing on the target distribution for difficult adaptation problems. Moreover, this normalization always helps to improve the model when we try to regularize the similarity based projection space in order to move closer the two distributions. We provide experiments on a toy problem and on a real image annotation task.

关键词： Good Similarity Functions Transfer learning Domain Adaptation Image Classification

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A Multi-Class Multi-Manifold learning Algorithm based on ISOMAP

A Multi-Class Multi-Manifold Learning Algorithm based on ISO...

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Chinese Conference on pattern recognition/1st CJK Joint workshop on pattern recognition

作者： Cheng, Qicai Wang, Hongyuan Feng, Yan Liu, Suolan Xue, Lei Jiangsu Polytech Univ Sch Informat Changzhou 213164 Peoples R China

ISBN: (纸本)9781424441990

The classical algorithm ISOMAP can find the intrinsic low-dimensional structures hidden in high-dimensional data uniformly distributed on or around a single manifold, but if the data are sampled from multi-class, each of which corresponds to an independent manifold, and clusters formed by data points belonging to each class are separated away, several disconnected neighborhood graphs will form, which leads to the failure of ISOMAP algorithm. In this paper, an improved version of ISOMAP, namely Multi-Class Multi-Manifold ISOMAP (MCMM-ISOMAP), is proposed. MCMM-ISOMAP constructs a single neighborhood graph not by increasing the value of neighborhood parameter, but by the following steps that first choose appropriate value with which short-circuit edges can not be introduced, second find such pail-wise data each of which are two endpoints of the shortest Euclidean distance between classes, and finally make them neighborhood points each other. Thereby a single neighborhood graph will form, and then ISOMAP algorithm is applied to find the intrinsic low-dimensional embedding structure. Experimental results on synthetic and real data reveal effectiveness of the proposed method.

关键词： ISOMAP multi-class multi-manifold MCMM-ISOMAP manifold learning

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FUZZY CONTROLLERS IN NUCLEAR MATERIAL ACCOUNTING

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FUZZY SETS AND SYstEMS 1995年第1期74卷 73-79页

作者： ZARDECKI, A Los Alamos National Laboratory MS E541 Los Alamos NM 87545 USA

Fuzzy controllers are applied to predicting and modeling a time series, with particular emphasis on anomaly detection in nuclear material inventory differences. As compared to neural networks, the fuzzy controllers can operate in real time;their learning process does not require many iterations to converge. For this reason fuzzy controllers are potentially useful in time series forecasting, where we want to detect and identify trends in real time. We describe an object-oriented implementation of the algorithm advanced by Wang and Mendel. Numerical results are presented both for inventory data and time series corresponding to chaotic situations, such as encountered in the context of strange attractors. In the latter case, the effects of noise on the predictive power of the fuzzy controller are explored.

关键词： pattern recognition APPLICATIONS TO NUCLEAR INVENTORY

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Process mining workshops 1

Process Mining Workshops

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丛书名： Lecture Notes in Business Information Processing

1000年

作者： Andrea Delgado Tijs Slaats

ISBN: (数字)9783031822254

ISBN: (纸本)9783031822247

This book constitutes the revised accepted papers of several workshops which were held in conjunction with the 6th international Conference on Process mining, ICPM 2024, held in Lyngby, Denmark, during October 2024. The 56 revised full papers presented in this book were carefully reviewed and selected from 126 submissions. The papers presented in this volume stem from the following workshops: – 9th international workshop on Process Querying, Manipulation, and Intelligence (PQMI) – 3rd international workshop on Education Meets Process mining (EduPM) – 3rd international workshop on Collaboration mining for Distributed Systems (CoMinDS) – 5th international workshop on Leveraging machine learning in Process mining (ML4PM) – 5th international workshop on Event data and Behavioral Analytics (EdbA) – 7th international workshop on Process-Oriented data Science for Healthcare (PODS4H) – 1st international workshop on Empirical Research in Process mining (ERPM) – 1st international workshop on Generative Artificial Intelligence for Process mining (GenAI4PM) – 4th international workshop on stream Management & Analytics for Process mining (SMA4PM) – 1st international workshop on Process mining for Sustainability (PM4S).

关键词： data mining and Knowledge Discovery Business Process Management machine learning Computer Applications Health Informatics

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Scaling large learning problems with hard parallel mixtures 1st

Scaling large learning problems with hard parallel mixtures

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1st international workshop on pattern recognition with Support Vector machines

作者： Collobert, R Bengio, Y Bengio, S IDIAP CH-1920 Martigny Switzerland Univ Montreal DIRO Montreal PQ Canada

ISBN: (纸本)354044016X

A challenge for statistical learning is to deal with large data sets, e.g. in data mining. The training time of ordinary Support Vector machines is at least quadratic, which raises a serious research challenge if we want to deal with data sets of millions of examples. We propose a "hard parallelizable mixture" methodology which yields significantly reduced training time through modularization and paxallelization: the training data is iteratively partitioned by a "gater" model in such a way that it becomes easy to learn an "expert" model separately in each region of the partition. A probabilistic extension and the use of a set of generative models allows representing the gater so that all pieces of the model are locally trained. For SVMs, time complexity appears empirically to local growth linearly with the number of examples, while generalization performance can be enhanced. For the probabilistic version of the algorithm, the iterative algorithm probably goes down in a cost function that is an upper bound on the negative log-likelihood.

关键词： mixture of experts support vector machines multilayered perceptrons probabilistic models data-mining large-scale learning dimensionality reduction Gaussian mixtures

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Building statistical Language Models of Code

Building Statistical Language Models of Code

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1st international workshop on data Analysis patterns in Software Engineering (DAPSE)

作者： Schulam, Peter Rosenfeld, Roni Devanbu, Premkumar Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA Univ Calif Davis Dept Comp Sci Davis CA USA

ISBN: (纸本)9781467362962

We present the Source Code statistical Language Model data analysis pattern. statistical language models have been an enabling tool for a wide array of important language technologies. Speech recognition, machine translation, and document summarization (to name a few) all rely on statistical language models to assign probability estimates to natural language utterances or sentences. In this data analysis pattern, we describe the process of building n-gram language models over software source files. We hope that by introducing the empirical software engineering community to best practices that have been established over the years in research for natural languages, statistical language models can become a tool that SE researchers are able to use to explore new research directions.

关键词： data analysis natural languages software engineering source coding statistical analysis

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Combining data Sources Nonlinearly for Cell Nucleus Classification of Renal Cell Carcinoma

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

作者： Gonen, Mehmet Ulas, Aydin Schueffler, Peter Castellani, Umberto Murino, Vittorio Aalto Univ Sch Sci Dept Informat & Comp Sci HIIT Espoo Finland Univ Verona Dept Comp Sci Verona Italy ETH Dept Comp Sci Zurich Switzerland IIT Genoa Italy

ISBN: (纸本)9783642244704;9783642244711

In kernel-based machine learning algorithms, we can learn a combination of different kernel functions in order to obtain a similarity measure that better matches the underlying problem instead of using a single fixed kernel function. This approach is called multiple kernel learning (MKL). In this paper, we formulate a nonlinear MKL variant and apply it for nuclei classification in tissue microarray images of renal cell carcinoma (RCC). The proposed variant is tested on several feature representations extracted from the automatically segmented nuclei. We compare our results with single-kernel support vector machines trained on each feature representation separately and three linear MKL algorithms from the literature. We demonstrate that our variant obtains more accurate classifiers than competing algorithms for RCC detection by combining information from different feature representations nonlinearly.

关键词： multiple kernel learning renal cell carcinoma support vector machines

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MULTISENSOR INTEGRATION - AN AUTOMATIC FEATURE-SELECTION AND stATE IDENTIFICATION METHODOLOGY FOR TOOL WEAR EstIMATION

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COMPUTERS IN INDUstRY 1991年第2-3期17卷 121-130页

作者： GUINEA, D RUIZ, A BARRIOS, LJ CSIC INST AUTOMAT INDE-28500 MADRIDSPAIN

Artificial systems obtain knowledge from the real world through sensors. The nature of the transducer, the location in space and the acquisition time must be considered to define the perceived state of the environment. Example base techniques are able to learn from the system experience. They are specially adequate for a complex multi-sensor system which lacks a formal knowledge background. data logging from diverse sensors, signal feature extraction, identification methods and evaluation strategies for the results have been integrated in a unique programming environment. Some critical aspects of empirical methods, such as automatic feature generation and selection or comparison of pattern recognition procedures, are approached from a global point of view. Some results obtained monitoring tool machines for the estimation of tool wear validate the presented work.

关键词： data FUSION MULTISENSOR INTEGRATION FEATURE EXTRACTION CLASSIFIERS pattern recognition machine TOOL MONITORING WEAR EstIMATION

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Advantage and Drawback of Support Vector machine Functionality 1

Advantage and Drawback of Support Vector Machine Functionali...

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1st international Conference on Computer, Communications, and Control Technology (I4CT)

作者： Karamizadeh, Sasan Abdullah, Shahidan M. Halimi, Mehran Shayan, Jafar Rajabi, Mohammad Javad UTM AIS Kuala Lumpur Malaysia

ISBN: (纸本)9781479945559

Support Vector machine(SVM) is one of the most efficient machine learning algorithms, which is mostly used for pattern recognition since its introduction in 1990s. SVMs vast variety of usage, such as face and speech recognition, face detection and image recognition has turned it into a very useful algorithm. This has also been applied to many pattern classification problems such as image recognition, speech recognition, text categorization, face detection, and faulty card detection. statistics was collected from journals and electronic sources published in the period of 2000 to 2013. pattern recognition aims to classify data based on either a priori knowledge or statistical information extracted from raw data, which is a powerful tool in data separation in many disciplines. The Support Vector machine ( SVM) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables.

关键词： upport Vector machine pattern recognition, classify, and face detection Biometricupport Vector machine pattern recognition, classify, and face detection BiometricS

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Image recommendation for Wikipedia articles 1

Image recommendation for Wikipedia articles

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1st Masters Symposium on Advances in data mining, machine learning, and Computer Vision, MS-AMLV 2019

作者： Onyshchak, Oleh Redi, Miriam Ukrainian Catholic University Lviv Ukraine Wikimedia Foundation London United Kingdom

Multimodal learning, which is simultaneous learning from different data sources such as audio, text, images, is a rapidly emerging field of machine learning. It is also considered as machine learning at the next upper level of abstraction. This allows tackle more complicated problems such as creating cartoons from a plot or speech recognition based on lips movement. In this paper, we propose to research whether state-of-the-art techniques of multimodal learning, will solve the problem of recommending the most relevant images for a Wikipedia article. In other words, we need to create a shared text-image representation of an abstract notion which paper describes, so that having only a text description machine would"understand" which images would visualize the same notion accurately. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： machine learning

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