from publication: Active Learning with Feedback on Features and Instances. | We extend the traditional active learning framework to include feedback on features in addition to labeling...

A typical algorithm for active learning and a block diagram are shown in Figure 1. An instance X (which is a document in our case) belongs to a class Y. X is represented as a vector x1...xN of features, where N is the total number of features. The features we use for documents are words,

Jan 11, 2024 · A subset of machine learning known as “active learning” allows a learning algorithm to interactively query a user to label data with the desired outputs. The algorithm actively chooses from the pool of unlabeled data the subset of …

This paper extends that work into the novel pixel-certainty activity learning (PCAL) based on the information about textural patterns obtained from the extended differential pattern (EDP).

Apr 22, 2022 · Active Learning machine learning algorithms which can actively query a user to label new data points also called optimal experimental design in statistics Given a labeled dataset x i;y i, i= 1;:::;m, query new points x j and obtain their labels y j from an expert, for j= 1;::::;r EE364b, Stanford University 6

Our focus in this paper is on the query and sample selection algorithms-depicted in white boxes with red borders (see text for details). from publication: Scalable Active Learning for...

To answer these questions, we develop and rigorously analyze a broad class of gen-eral active learning methods that address the essential algorithmic and statistical difficulties of the problem. Consider first the task of learning a threshold function of a single variable.

We introduce a model that learns active learning algorithms via metalearning. For a distribution of related tasks, our model jointly learns: a data representation, an item selection heuristic, and. a method for constructing prediction functions from labeled training sets.

Figure 7: Input/ Output pin diagram of training algorithm We have studied the training algorithm in the previous topic. Now, in this topic, implementation of training algorithm by Verilog HDL is given. It requires input vectors and output vectors to produce …

We propose moving away from engineered selection heuristics towards learning active learning algo-rithms end-to-end via metalearning. Our model interacts with labeled items for many related tasks in order to learn an active learning strategy for the task at hand.