List of datasets in computer vision and image processing
Outline of machine learning
v
t
e
In machine learning, feature learning or representation learning[2] is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task.
Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensor data have not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.
Feature learning can be either supervised, unsupervised or self-supervised.
In supervised feature learning, features are learned using labeled input data. Labeled data includes input-label pairs where the input is given to the model and it must produce the ground truth label as the correct answer.[3] This can be leveraged to generate feature representations with the model which result in high label prediction accuracy. Examples include supervised neural networks, multilayer perceptron and (supervised) dictionary learning.
In unsupervised feature learning, features are learned with unlabeled input data by analyzing the relationship between points in the dataset.[4] Examples include dictionary learning, independent component analysis, matrix factorization[5] and various forms of clustering.[6][7][8]
In self-supervised feature learning, features are learned using unlabeled data like unsupervised learning, however input-label pairs are constructed from each data point, which enables learning the structure of the data through supervised methods such as gradient descent.[9] Classical examples include word embeddings and autoencoders.[10][11] SSL has since been applied to many modalities through the use of deep neural network architectures such as CNNs and transformers.[9]
^Goodfellow, Ian (2016). Deep learning. Yoshua Bengio, Aaron Courville. Cambridge, Massachusetts. pp. 524–534. ISBN 0-262-03561-8. OCLC 955778308.
^Y. Bengio; A. Courville; P. Vincent (2013). "Representation Learning: A Review and New Perspectives". IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (8): 1798–1828. arXiv:1206.5538. doi:10.1109/tpami.2013.50. PMID 23787338. S2CID 393948.
^Stuart J. Russell, Peter Norvig (2010) Artificial Intelligence: A Modern Approach, Third Edition, Prentice Hall ISBN 978-0-13-604259-4.
^Hinton, Geoffrey; Sejnowski, Terrence (1999). Unsupervised Learning: Foundations of Neural Computation. MIT Press. ISBN 978-0-262-58168-4.
^Nathan Srebro; Jason D. M. Rennie; Tommi S. Jaakkola (2004). Maximum-Margin Matrix Factorization. NIPS.
^Cite error: The named reference coates2011 was invoked but never defined (see the help page).
^Csurka, Gabriella; Dance, Christopher C.; Fan, Lixin; Willamowski, Jutta; Bray, Cédric (2004). Visual categorization with bags of keypoints(PDF). ECCV Workshop on Statistical Learning in Computer Vision.
^Daniel Jurafsky; James H. Martin (2009). Speech and Language Processing. Pearson Education International. pp. 145–146.
^Mikolov, Tomas; Sutskever, Ilya; Chen, Kai; Corrado, Greg S; Dean, Jeff (2013). "Distributed Representations of Words and Phrases and their Compositionality". Advances in Neural Information Processing Systems. 26. Curran Associates, Inc. arXiv:1310.4546.
^Goodfellow, Ian (2016). Deep learning. Yoshua Bengio, Aaron Courville. Cambridge, Massachusetts. pp. 499–516. ISBN 0-262-03561-8. OCLC 955778308.
In machine learning, featurelearning or representation learning is a set of techniques that allows a system to automatically discover the representations...
dictionary learning. In unsupervised featurelearning, features are learned with unlabeled input data. Examples include dictionary learning, independent...
Geometric featurelearning is a technique combining machine learning and computer vision to solve visual tasks. The main goal of this method is to find...
Feature engineering, a preprocessing step in supervised machine learning and statistical modeling, transforms raw data into a more effective set of inputs...
deviation. Another option that is widely used in machine-learning is to scale the components of a feature vector such that the complete vector has length one...
compatibility with a learning model class, encode inherent symmetries present in the input space. The central premise when using a feature selection technique...
phenomena in question. In contrast, singular modal learning would analyze text (typically represented as feature vector) or imaging data (consisting of pixel...
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...
"flexible" learning algorithm with low bias and high variance. A third issue is the dimensionality of the input space. If the input feature vectors have...
Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...
for machine learning, an expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods...
In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration...
programs, materials or learning and development programs. The learning management system concept emerged directly from e-Learning. Learning management systems...
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or...
Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of...
In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms...
corner or blob Feature (machine learning), in statistics: individual measurable properties of the phenomena being observed Software feature, a distinguishing...
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent to human preferences. In classical...
Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem...
In machine learning, the perceptron (or McCulloch–Pitts neuron) is an algorithm for supervised learning of binary classifiers. A binary classifier is a...