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Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM). [ 2 ] [ 3 ] It is a framework with wide support for deep learning algorithms. [ 4 ] Deeplearning4j includes implementations of the restricted Boltzmann machine , deep belief net , deep autoencoder, stacked denoising autoencoder and recursive ...
Real-time Java is a catch-all term for a combination of technologies that enables programmers to write programs that meet the demands of real-time systems in the Java programming language. Java's sophisticated memory management , native support for threading and concurrency, type safety , and relative simplicity have created a demand for its ...
Active learning: Instead of assuming that all of the training examples are given at the start, active learning algorithms interactively collect new examples, typically by making queries to a human user. Often, the queries are based on unlabeled data, which is a scenario that combines semi-supervised learning with active learning.
High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce ...
From the perspective of statistical learning theory, supervised learning is best understood. [4] Supervised learning involves learning from a training set of data. Every point in the training is an input–output pair, where the input maps to an output. The learning problem consists of inferring the function that maps between the input and the ...
Depending on the type and variation in training data, machine learning can be roughly categorized into three frameworks: supervised learning, unsupervised learning, and reinforcement learning. Multiple instance learning (MIL) falls under the supervised learning framework, where every training instance has a label, either discrete or real valued ...
Manifold learning can draw a decision boundary between the natural classes of the unlabeled data, under the assumption that close-together points probably belong to the same class, and so the decision boundary should avoid areas with many unlabeled points. This is one version of semi-supervised learning.
In addition to the supervised learning setting, sample complexity is relevant to semi-supervised learning problems including active learning, [7] where the algorithm can ask for labels to specifically chosen inputs in order to reduce the cost of obtaining many labels.