Similarity learning is an area of supervised machine learning in artificial intelligence. It is closely related to regression and classification, but...

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...

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...

height of a person, or the future temperature. Similarity learning is an area of supervised machine learning closely related to regression and classification...

not just by arbitrary descriptors. Deep learning methods have become an accurate way to gauge semantic similarity between two text passages, in which each...

K-nearest neighbor algorithm Neural networks (Multilayer perceptron) Similarity learning Given a set of N {\displaystyle N} training examples of the form...

unlabeled data. Within such an approach, a machine learning model tries to find any similarities, differences, patterns, and structure in data by itself...

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...

Systems. 6: 737–744. Chopra, S.; Hadsell, R.; LeCun, Y. (June 2005). "Learning a Similarity Metric Discriminatively, with Application to Face Verification"...

gradient methods for learning Semantic analysis Similarity learning Sparse dictionary learning Stability (learning theory) Statistical learning theory Statistical...

for learning similarity for the purpose of learning embeddings, such as learning to rank, word embeddings, thought vectors, and metric learning. Consider...

learning. Factors that can affect transfer include: Context and degree of original learning: how well the learner acquired the knowledge. Similarity:...

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...

for vector output Kernel density estimation Representer theorem Similarity learning Cover's theorem "Kernel method". Engati. Retrieved 2023-04-04. Theodoridis...

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...

(also known as a string similarity metric or string distance function) is a metric that measures distance ("inverse similarity") between two text strings...

Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of...

Hellinger distance, also a measure of distance between data sets Similarity learning, for other approaches to learn a distance metric from examples. "Reprint...

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...

research that explores methodological and structural similarities between certain physical systems and learning systems, in particular neural networks. For example...

and scholars have drawn similarities between implicit learning and implicit memory. Examples from daily life, like learning how to ride a bicycle or...

Schneider, Stefan; Taylor, Graham W.; Kremer, Stefan C. (2020). "Similarity Learning Networks for Animal Individual Re-Identification - Beyond the Capabilities...

Bozinovski and Ante Fulgosi (1976). "The influence of pattern similarity and transfer learning upon the training of a base perceptron B2." (original in Croatian)...

of data items and a similarity function. These models learn the embeddings by leveraging statistical techniques and machine learning algorithms. Here are...

interpolation Neighbor joining Principal component analysis Range search Similarity learning Singular value decomposition Sparse distributed memory Statistical...

; Evans, Selby H. (March 1970), "Pythagorean distance and the judged similarity of schematic stimuli", Perception & Psychophysics, 7 (2): 103–107, doi:10...

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...

feature vectors close to each other. Vector databases can be used for similarity search, multi-modal search, recommendations engines, large language models...

association are contiguity, repetition, attention, pleasure-pain, and similarity. The basic laws were formulated by Aristotle in approximately 300 B.C...

non-linear classifiers that employ a kernel function to compute the similarity of unseen samples to training samples. The algorithm was invented in 1964...