Similarity learning is an area of supervised machine learning in artificial intelligence. It is closely related to regression and classification, but...
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the similarity of documents in the vector space model. In machine learning, common kernel functions such as the RBF kernel can be viewed as similarity functions...
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analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of...
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height of a person, or the future temperature. Similarity learning is an area of supervised machine learning closely related to regression and classification...
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not just by arbitrary descriptors. Deep learning methods have become an accurate way to gauge semantic similarity between two text passages, in which each...
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K-nearest neighbor algorithm Neural networks (Multilayer perceptron) Similarity learning Given a set of N {\displaystyle N} training examples of the form...
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unlabeled data. Within such an approach, a machine learning model tries to find any similarities, differences, patterns, and structure in data by itself...
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www.sisap.org Similarity learning Latent semantic analysis Pei Lee, Laks V. S. Lakshmanan, Jeffrey Xu Yu: On Top-k Structural Similarity Search. ICDE 2012:774-785...
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Siamese neural network (section Learning)
Systems. 6: 737–744. Chopra, S.; Hadsell, R.; LeCun, Y. (June 2005). "Learning a Similarity Metric Discriminatively, with Application to Face Verification"...
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gradient methods for learning Semantic analysis Similarity learning Sparse dictionary learning Stability (learning theory) Statistical learning theory Statistical...
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Triplet loss (category Machine learning algorithms)
for learning similarity for the purpose of learning embeddings, such as learning to rank, word embeddings, thought vectors, and metric learning. Consider...
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learning. Factors that can affect transfer include: Context and degree of original learning: how well the learner acquired the knowledge. Similarity:...
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linguistics, lexical similarity is a measure of the degree to which the word sets of two given languages are similar. A lexical similarity of 1 (or 100%) would...
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Kernel method (category Kernel methods for machine learning)
for vector output Kernel density estimation Representer theorem Similarity learning Cover's theorem "Kernel method". Engati. Retrieved 2023-04-04. Theodoridis...
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Zero-shot learning (ZSL) is a problem setup in deep learning where, at test time, a learner observes samples from classes which were not observed during...
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String metric (redirect from String similarity)
(also known as a string similarity metric or string distance function) is a metric that measures distance ("inverse similarity") between two text strings...
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research that explores methodological and structural similarities between certain physical systems and learning systems, in particular neural networks. For example...
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Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of...
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Hellinger distance, also a measure of distance between data sets Similarity learning, for other approaches to learn a distance metric from examples. "Reprint...
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Schneider, Stefan; Taylor, Graham W.; Kremer, Stefan C. (2020). "Similarity Learning Networks for Animal Individual Re-Identification - Beyond the Capabilities...
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Bozinovski and Ante Fulgosi (1976). "The influence of pattern similarity and transfer learning upon the training of a base perceptron B2." (original in Croatian)...
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Competitive learning is a form of unsupervised learning in artificial neural networks, in which nodes compete for the right to respond to a subset of...
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of data items and a similarity function. These models learn the embeddings by leveraging statistical techniques and machine learning algorithms. Here are...
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interpolation Neighbor joining Principal component analysis Range search Similarity learning Singular value decomposition Sparse distributed memory Statistical...
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and scholars have drawn similarities between implicit learning and implicit memory. Examples from daily life, like learning how to ride a bicycle or...
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Vector database (category Machine learning)
feature vectors close to each other. Vector databases can be used for similarity search, multi-modal search, recommendations engines, large language models...
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; Evans, Selby H. (March 1970), "Pythagorean distance and the judged similarity of schematic stimuli", Perception & Psychophysics, 7 (2): 103–107, doi:10...
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In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function...
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association are contiguity, repetition, attention, pleasure-pain, and similarity. The basic laws were formulated by Aristotle in approximately 300 B.C...
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Kernel perceptron (category Kernel methods for machine learning)
non-linear classifiers that employ a kernel function to compute the similarity of unseen samples to training samples. The algorithm was invented in 1964...
9 KB (1,175 words) - 22:03, 5 May 2021