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Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...
In machine learning, the delta rule is a gradient descent learning rule for updating the weights of the inputs to artificial neurons in a single-layer neural network. [1]
An Introduction to Computational Learning Theory. MIT Press, 1994. A textbook. M. Mohri, A. Rostamizadeh, and A. Talwalkar. Foundations of Machine Learning. MIT Press, 2018. Chapter 2 contains a detailed treatment of PAC-learnability. Readable through open access from the publisher. D. Haussler.
2.00 98 Teacher Reinforcement 1.2 Learner Feedback-corrective (mastery learning) 1.00 84 Teacher Cues and explanations 1.00 Teacher, Learner Student classroom participation 1.00 Learner Student time on task 1.00 Learner Improved reading/study skills 1.00 Home environment / peer group Cooperative learning: 0.80 79 Teacher Homework (graded) 0.80
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video.This integration allows for a more holistic understanding of complex data, improving model performance in tasks like visual question answering, cross-modal retrieval, [1] text-to-image generation, [2] aesthetic ranking, [3] and ...
Sushi Go! - The Pick and Pass Card Game. In this fun (and highly adorable) card game, players compete to collect sushi, sashimi, and other foods worth varying amounts of points.
The perceptron learning rule originates from the Hebbian assumption, and was used by Frank Rosenblatt in his perceptron in 1958. The net is passed to the activation function and the function's output is used for adjusting the weights. The learning signal is the difference between the desired response and the actual response of a neuron.
Inverse reinforcement learning (IRL) is the process of deriving a reward function from observed behavior. While ordinary "reinforcement learning" involves using rewards and punishments to learn behavior, in IRL the direction is reversed, and a robot observes a person's behavior to figure out what goal that behavior seems to be trying to achieve. [3]