Literatura académica sobre el tema "Similarity metric learning"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Similarity metric learning".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Similarity metric learning"
Loriette, Antoine, Wanyu Liu, Frédéric Bevilacqua y Baptiste Caramiaux. "Describing movement learning using metric learning". PLOS ONE 18, n.º 2 (3 de febrero de 2023): e0272509. http://dx.doi.org/10.1371/journal.pone.0272509.
Texto completoTao, Tao, Qianqian Wang, Yue Ruan, Xue Li y Xiujun Wang. "Graph Embedding with Similarity Metric Learning". Symmetry 15, n.º 8 (21 de agosto de 2023): 1618. http://dx.doi.org/10.3390/sym15081618.
Texto completoLE, Yi-ze, Yong FENG, Da-jiang LIU y Bao-hua QIANG. "Adversarial Metric Learning with Naive Similarity Discriminator". IEICE Transactions on Information and Systems E103.D, n.º 6 (1 de junio de 2020): 1406–13. http://dx.doi.org/10.1587/transinf.2019edp7278.
Texto completoLi, Yujiang, Chun Ding y Zhili Zhou. "Vehicle Matching Based on Similarity Metric Learning". Journal of New Media 4, n.º 1 (2022): 51–58. http://dx.doi.org/10.32604/jnm.2022.028775.
Texto completoWei, Zeqiang, Min Xu, Lin Geng, Haoming Liu y Hua Yin. "Adversarial Similarity Metric Learning for Kinship Verification". IEEE Access 7 (2019): 100029–35. http://dx.doi.org/10.1109/access.2019.2929939.
Texto completoCao, Qiong, Zheng-Chu Guo y Yiming Ying. "Generalization bounds for metric and similarity learning". Machine Learning 102, n.º 1 (20 de junio de 2015): 115–32. http://dx.doi.org/10.1007/s10994-015-5499-7.
Texto completoLowe, David G. "Similarity Metric Learning for a Variable-Kernel Classifier". Neural Computation 7, n.º 1 (enero de 1995): 72–85. http://dx.doi.org/10.1162/neco.1995.7.1.72.
Texto completoGarcia, Noa y George Vogiatzis. "Learning non-metric visual similarity for image retrieval". Image and Vision Computing 82 (febrero de 2019): 18–25. http://dx.doi.org/10.1016/j.imavis.2019.01.001.
Texto completoWang, Huibing, Lin Feng, Jing Zhang y Yang Liu. "Semantic Discriminative Metric Learning for Image Similarity Measurement". IEEE Transactions on Multimedia 18, n.º 8 (agosto de 2016): 1579–89. http://dx.doi.org/10.1109/tmm.2016.2569412.
Texto completoZhang, Lei y David Zhang. "MetricFusion: Generalized metric swarm learning for similarity measure". Information Fusion 30 (julio de 2016): 80–90. http://dx.doi.org/10.1016/j.inffus.2015.12.004.
Texto completoTesis sobre el tema "Similarity metric learning"
Cao, Qiong. "Some topics on similarity metric learning". Thesis, University of Exeter, 2015. http://hdl.handle.net/10871/18662.
Texto completoCuan, Bonan. "Deep similarity metric learning for multiple object tracking". Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI065.
Texto completoMultiple object tracking, i.e. simultaneously tracking multiple objects in the scene, is an important but challenging visual task. Objects should be accurately detected and distinguished from each other to avoid erroneous trajectories. Since remarkable progress has been made in object detection field, “tracking-by-detection” approaches are widely adopted in multiple object tracking research. Objects are detected in advance and tracking reduces to an association problem: linking detections of the same object through frames into trajectories. Most tracking algorithms employ both motion and appearance models for data association. For multiple object tracking problems where exist many objects of the same category, a fine-grained discriminant appearance model is paramount and indispensable. Therefore, we propose an appearance-based re-identification model using deep similarity metric learning to deal with multiple object tracking in mono-camera videos. Two main contributions are reported in this dissertation: First, a deep Siamese network is employed to learn an end-to-end mapping from input images to a discriminant embedding space. Different metric learning configurations using various metrics, loss functions, deep network structures, etc., are investigated, in order to determine the best re-identification model for tracking. In addition, with an intuitive and simple classification design, the proposed model achieves satisfactory re-identification results, which are comparable to state-of-the-art approaches using triplet losses. Our approach is easy and fast to train and the learned embedding can be readily transferred onto the domain of tracking tasks. Second, we integrate our proposed re-identification model in multiple object tracking as appearance guidance for detection association. For each object to be tracked in a video, we establish an identity-related appearance model based on the learned embedding for re-identification. Similarities among detected object instances are exploited for identity classification. The collaboration and interference between appearance and motion models are also investigated. An online appearance-motion model coupling is proposed to further improve the tracking performance. Experiments on Multiple Object Tracking Challenge benchmark prove the effectiveness of our modifications, with a state-of-the-art tracking accuracy
Zheng, Lilei. "Triangular similarity metric learning : A siamese architecture approach". Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI045/document.
Texto completoIn many machine learning and pattern recognition tasks, there is always a need for appropriate metric functions to measure pairwise distance or similarity between data, where a metric function is a function that defines a distance or similarity between each pair of elements of a set. In this thesis, we propose Triangular Similarity Metric Learning (TSML) for automatically specifying a metric from data. A TSML system is loaded in a siamese architecture which consists of two identical sub-systems sharing the same set of parameters. Each sub-system processes a single data sample and thus the whole system receives a pair of data as the input. The TSML system includes a cost function parameterizing the pairwise relationship between data and a mapping function allowing the system to learn high-level features from the training data. In terms of the cost function, we first propose the Triangular Similarity, a novel similarity metric which is equivalent to the well-known Cosine Similarity in measuring a data pair. Based on a simplified version of the Triangular Similarity, we further develop the triangular loss function in order to perform metric learning, i.e. to increase the similarity between two vectors in the same class and to decrease the similarity between two vectors of different classes. Compared with other distance or similarity metrics, the triangular loss and its gradient naturally offer us an intuitive and interesting geometrical interpretation of the metric learning objective. In terms of the mapping function, we introduce three different options: a linear mapping realized by a simple transformation matrix, a nonlinear mapping realized by Multi-layer Perceptrons (MLP) and a deep nonlinear mapping realized by Convolutional Neural Networks (CNN). With these mapping functions, we present three different TSML systems for various applications, namely, pairwise verification, object identification, dimensionality reduction and data visualization. For each application, we carry out extensive experiments on popular benchmarks and datasets to demonstrate the effectiveness of the proposed systems
Zhang, Hauyi. "Similarity Search in Continuous Data with Evolving Distance Metric". Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1253.
Texto completoForssell, Melker y Gustav Janér. "Product Matching Using Image Similarity". Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413481.
Texto completoMichel, Fabrice. "Multi-Modal Similarity Learning for 3D Deformable Registration of Medical Images". Phd thesis, Ecole Centrale Paris, 2013. http://tel.archives-ouvertes.fr/tel-01005141.
Texto completoEriksson, Louise. "An experimental investigation of the relation between learning and separability in spatial representations". Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-622.
Texto completoOne way of modeling human knowledge is by using multidimensional spaces, in which an object is represented as a point in the space, and the distances among the points reflect the similarities among the represented objects. The distances are measured with some metric, commonly some instance of the Minkowski metric. The instances differ with the magnitude of the so-called r-parameter. The instances most commonly mentioned in the literature are the ones where r equals 1, 2 and infinity.
Cognitive scientists have found out that different metrics are suited to describe different dimensional combinations. From these findings an important distinction between integral and separable dimensions has been stated (Garner, 1974). Separable dimensions, e.g. size and form, are best described by the city-block metric, where r equals 1, and integral dimensions, such as the color dimensions, are best described by the Euclidean metric, where r equals 2. Developmental psychologists have formulated a hypothesis saying that small children perceive many dimensional combinations as integral whereas adults perceive the same combinations as separable. Thus, there seems to be a shift towards increasing separability with age or maturity.
Earlier experiments show the same phenomenon in adult short-term learning with novel stimuli. In these experiments, the stimuli were first perceived as rather integral and were then turning more separable, indicated by the Minkowski-r. This indicates a shift towards increasing separability with familiarity or skill.
This dissertation aims at investigating the generality of this phenomenon. Five similarity-rating experiments are conducted, for which the best fitting metric for the first half of the session is compared to the last half of the session. If the Minkowski-r is lower for the last half compared to the first half, it is considered to indicate increasing separability.
The conclusion is that the phenomenon of increasing separability during short-term learning cannot be found in these experiments, at least not given the operational definition of increasing separability as a function of a decreasing Minkowski-r. An alternative definition of increasing separability is suggested, where an r-value ‘retreating’ 2.0 indicates increasing separability, i.e. when the r-value of the best fitting metric for the last half of a similarity-rating session is further away from 2.0 compared to the first half of the session.
Qamar, Ali Mustafa. "Mesures de similarité et cosinus généralisé : une approche d'apprentissage supervisé fondée sur les k plus proches voisins". Phd thesis, Grenoble, 2010. http://www.theses.fr/2010GRENM083.
Texto completoAlmost all machine learning problems depend heavily on the metric used. Many works have proved that it is a far better approach to learn the metric structure from the data rather than assuming a simple geometry based on the identity matrix. This has paved the way for a new research theme called metric learning. Most of the works in this domain have based their approaches on distance learning only. However some other works have shown that similarity should be preferred over distance metrics while dealing with textual datasets as well as with non-textual ones. Being able to efficiently learn appropriate similarity measures, as opposed to distances, is thus of high importance for various collections. If several works have partially addressed this problem for different applications, no previous work is known which has fully addressed it in the context of learning similarity metrics for kNN classification. This is exactly the focus of the current study. In the case of information filtering systems where the aim is to filter an incoming stream of documents into a set of predefined topics with little supervision, cosine based category specific thresholds can be learned. Learning such thresholds can be seen as a first step towards learning a complete similarity measure. This strategy was used to develop Online and Batch algorithms for information filtering during the INFILE (Information Filtering) track of the CLEF (Cross Language Evaluation Forum) campaign during the years 2008 and 2009. However, provided enough supervised information is available, as is the case in classification settings, it is usually beneficial to learn a complete metric as opposed to learning thresholds. To this end, we developed numerous algorithms for learning complete similarity metrics for kNN classification. An unconstrained similarity learning algorithm called SiLA is developed in which case the normalization is independent of the similarity matrix. SiLA encompasses, among others, the standard cosine measure, as well as the Dice and Jaccard coefficients. SiLA is an extension of the voted perceptron algorithm and allows to learn different types of similarity functions (based on diagonal, symmetric or asymmetric matrices). We then compare SiLA with RELIEF, a well known feature re-weighting algorithm. It has recently been suggested by Sun and Wu that RELIEF can be seen as a distance metric learning algorithm optimizing a cost function which is an approximation of the 0-1 loss. We show here that this approximation is loose, and propose a stricter version closer to the the 0-1 loss, leading to a new, and better, RELIEF-based algorithm for classification. We then focus on a direct extension of the cosine similarity measure, defined as a normalized scalar product in a projected space. The associated algorithm is called generalized Cosine simiLarity Algorithm (gCosLA). All of the algorithms are tested on many different datasets. A statistical test, the s-test, is employed to assess whether the results are significantly different. GCosLA performed statistically much better than SiLA on many of the datasets. Furthermore, SiLA and gCosLA were compared with many state of the art algorithms, illustrating their well-foundedness
Bäck, Jesper. "Domain similarity metrics for predicting transfer learning performance". Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153747.
Texto completoFerns, Norman Francis. "State-similarity metrics for continuous Markov decision processes". Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=103383.
Texto completoCapítulos de libros sobre el tema "Similarity metric learning"
Duffner, Stefan, Christophe Garcia, Khalid Idrissi y Atilla Baskurt. "Similarity Metric Learning". En Multi-faceted Deep Learning, 103–25. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-030-74478-6_5.
Texto completoHoffer, Elad y Nir Ailon. "Deep Metric Learning Using Triplet Network". En Similarity-Based Pattern Recognition, 84–92. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24261-3_7.
Texto completoWu, Xiang, Zhi-Guo Shi y Lei Liu. "Quasi Cosine Similarity Metric Learning". En Computer Vision - ACCV 2014 Workshops, 194–205. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16634-6_15.
Texto completoKliper-Gross, Orit, Tal Hassner y Lior Wolf. "One Shot Similarity Metric Learning for Action Recognition". En Similarity-Based Pattern Recognition, 31–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24471-1_3.
Texto completoNaudé, Johannes J., Michaël A. van Wyk y Barend J. van Wyk. "Generalized Variable-Kernel Similarity Metric Learning". En Lecture Notes in Computer Science, 788–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27868-9_86.
Texto completoCarrara, Fabio, Claudio Gennaro, Fabrizio Falchi y Giuseppe Amato. "Learning Distance Estimators from Pivoted Embeddings of Metric Objects". En Similarity Search and Applications, 361–68. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60936-8_28.
Texto completoNguyen, Hieu V. y Li Bai. "Cosine Similarity Metric Learning for Face Verification". En Computer Vision – ACCV 2010, 709–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19309-5_55.
Texto completoAhmadzadeh, Azim, Yang Chen, Krishna Rukmini Puthucode, Ruizhe Ma y Rafal A. Angryk. "TS-MIoU: A Time Series Similarity Metric Without Mapping". En Machine Learning and Knowledge Discovery in Databases, 87–102. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26422-1_6.
Texto completoRicci, Francesco y Paolo Avesani. "Learning a local similarity metric for case-based reasoning". En Case-Based Reasoning Research and Development, 301–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60598-3_27.
Texto completovan den Berg, Sophie y Marwan Hassani. "On Inferring a Meaningful Similarity Metric for Customer Behaviour". En Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 234–50. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86517-7_15.
Texto completoActas de conferencias sobre el tema "Similarity metric learning"
Xiaoqiang Zhu, Pinghua Gong, Zengshun Zhao y Changshui Zhang. "Learning similarity metric with SVM". En 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252829.
Texto completoCao, Qiong, Yiming Ying y Peng Li. "Similarity Metric Learning for Face Recognition". En 2013 IEEE International Conference on Computer Vision (ICCV). IEEE, 2013. http://dx.doi.org/10.1109/iccv.2013.299.
Texto completoXu, Xinyi, Huanhuan Cao, Yanhua Yang, Erkun Yang y Cheng Deng. "Zero-shot Metric Learning". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/555.
Texto completoWei Wang y Bin-Xing Fang. "A grid execution environment similarity metric". En Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527458.
Texto completoLee, Jongpil, Nicholas J. Bryan, Justin Salamon, Zeyu Jin y Juhan Nam. "Disentangled Multidimensional Metric Learning for Music Similarity". En ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053442.
Texto completoFang, Yuan, Yan Yan, Si Chen, Hanzi Wang y Chang Shu. "Sparse similarity metric learning for kinship verification". En 2016 Visual Communications and Image Processing (VCIP). IEEE, 2016. http://dx.doi.org/10.1109/vcip.2016.7805462.
Texto completoLilei Zheng, Khalid Idrissi, Christophe Garcia, Stefan Duffner y Atilla Baskurt. "Triangular similarity metric learning for face verification". En 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). IEEE, 2015. http://dx.doi.org/10.1109/fg.2015.7163085.
Texto completoJin, Guoxin y Thrasyvoulos N. Pappas. "Building structural similarity database for metric learning". En IS&T/SPIE Electronic Imaging, editado por Bernice E. Rogowitz, Thrasyvoulos N. Pappas y Huib de Ridder. SPIE, 2015. http://dx.doi.org/10.1117/12.2079392.
Texto completoZheng, Lilei, Khalid Idrissi, Christophe Garcia, Stefan Duffner y Atilla Baskurt. "Logistic similarity metric learning for face verification". En ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178311.
Texto completoZhang, Kaizhong. "Similarity metric induced metrics with application in machine learning and bioinformatics". En 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2016. http://dx.doi.org/10.1109/icci-cc.2016.7862048.
Texto completoInformes sobre el tema "Similarity metric learning"
Griffin, Andrew, Sean Griffin, Kristofer Lasko, Megan Maloney, S. Blundell, Michael Collins y Nicole Wayant. Evaluation of automated feature extraction algorithms using high-resolution satellite imagery across a rural-urban gradient in two unique cities in developing countries. Engineer Research and Development Center (U.S.), abril de 2021. http://dx.doi.org/10.21079/11681/40182.
Texto completo