List of datasets in computer vision and image processing
Outline of machine learning
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Learning to rank[1] or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems.[2] Training data may, for example, consist of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment (e.g. "relevant" or "not relevant") for each item. The goal of constructing the ranking model is to rank new, unseen lists in a similar way to rankings in the training data.
^Tie-Yan Liu (2009), "Learning to Rank for Information Retrieval", Foundations and Trends in Information Retrieval, 3 (3): 225–331, doi:10.1561/1500000016, ISBN 978-1-60198-244-5. Slides from Tie-Yan Liu's talk at WWW 2009 conference are available online Archived 2017-08-08 at the Wayback Machine
^Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012) Foundations of Machine Learning, The
MIT Press ISBN 9780262018258.
Learningtorank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning...
Low-Rank Factorization, Network Architecture Search (NAS) & Parameter Sharing are few of the techniques used for optimization of machine learning models...
parse tree or a labeled graph, then standard methods must be extended. Learningtorank: When the input is a set of objects and the desired output is a ranking...
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...
Multimodal learning, in the context of machine learning, is a type of deep learning using a combination of various modalities of data, such as text, audio...
of Data Handling. In 1967, a deep-learning network, which used stochastic gradient descent for the first time, able to classify non-linearily separable...
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions...
the learning rate is often varied during training either in accordance to a learning rate schedule or by using an adaptive learning rate. The learning rate...
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, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent to human preferences. In classical...
In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms...
Unsupervised learning is a method in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data...
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or...
layer is connected to these context units fixed with a weight of one. At each time step, the input is fed forward and a learning rule is applied. The...
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate...
Greg Hullender. 2005. Learningtorank using gradient descent. In Proceedings of the 22nd international conference on Machine learning (ICML '05). ACM, New...
Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty"...
In machine learning, the perceptron (or McCulloch–Pitts neuron) is an algorithm for supervised learning of binary classifiers. A binary classifier is a...
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