Machine Learning (journal)

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Inductive logic programming (ILP) is a subfield of symbolic artificial intelligence which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesised logic program which entails all the positive and none of the negative examples.

<span class="mw-page-title-main">Boosting (machine learning)</span> Method in machine learning

In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant : "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification. In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification.

Solomonoff's theory of inductive inference is a mathematical theory of induction introduced by Ray Solomonoff, based on probability theory and theoretical computer science. In essence, Solomonoff's induction derives the posterior probability of any computable theory, given a sequence of observed data. This posterior probability is derived from Bayes' rule and some universal prior, that is, a prior that assigns a positive probability to any computable theory.

<span class="mw-page-title-main">Stochastic gradient descent</span> Optimization algorithm

Stochastic gradient descent is an iterative method for optimizing an objective function with suitable smoothness properties. It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient by an estimate thereof. Especially in high-dimensional optimization problems this reduces the very high computational burden, achieving faster iterations in exchange for a lower convergence rate.

Algorithmic information theory (AIT) is a branch of theoretical computer science that concerns itself with the relationship between computation and information of computably generated objects (as opposed to stochastically generated), such as strings or any other data structure. In other words, it is shown within algorithmic information theory that computational incompressibility "mimics" (except for a constant that only depends on the chosen universal programming language) the relations or inequalities found in information theory. According to Gregory Chaitin, it is "the result of putting Shannon's information theory and Turing's computability theory into a cocktail shaker and shaking vigorously."

<span class="mw-page-title-main">Multi-armed bandit</span> Machine Learning

In probability theory and machine learning, the multi-armed bandit problem is a problem in which a fixed limited set of resources must be allocated between competing (alternative) choices in a way that maximizes their expected gain, when each choice's properties are only partially known at the time of allocation, and may become better understood as time passes or by allocating resources to the choice. This is a classic reinforcement learning problem that exemplifies the exploration–exploitation tradeoff dilemma. The name comes from imagining a gambler at a row of slot machines, who has to decide which machines to play, how many times to play each machine and in which order to play them, and whether to continue with the current machine or try a different machine. The multi-armed bandit problem also falls into the broad category of stochastic scheduling.

<span class="mw-page-title-main">Stephen Muggleton</span> Artificial intelligence researcher

Stephen H. Muggleton FBCS, FIET, FAAAI, FECCAI, FSB, FREng is Professor of Machine Learning and Head of the Computational Bioinformatics Laboratory at Imperial College London.

Progol is an implementation of inductive logic programming that combines inverse entailment with general-to-specific search through a refinement graph. It was developed by Stephen Muggleton.

<span class="mw-page-title-main">Decision stump</span> Boolean classifier from one decision

A decision stump is a machine learning model consisting of a one-level decision tree. That is, it is a decision tree with one internal node which is immediately connected to the terminal nodes. A decision stump makes a prediction based on the value of just a single input feature. Sometimes they are also called 1-rules.

Leslie Pack Kaelbling is an American roboticist and the Panasonic Professor of Computer Science and Engineering at the Massachusetts Institute of Technology. She is widely recognized for adapting partially observable Markov decision process from operations research for application in artificial intelligence and robotics. Kaelbling received the IJCAI Computers and Thought Award in 1997 for applying reinforcement learning to embedded control systems and developing programming tools for robot navigation. In 2000, she was elected as a Fellow of the Association for the Advancement of Artificial Intelligence.

<span class="mw-page-title-main">Ensemble learning</span> Statistics and machine learning technique

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.

An incremental decision tree algorithm is an online machine learning algorithm that outputs a decision tree. Many decision tree methods, such as C4.5, construct a tree using a complete dataset. Incremental decision tree methods allow an existing tree to be updated using only new individual data instances, without having to re-process past instances. This may be useful in situations where the entire dataset is not available when the tree is updated, the original data set is too large to process or the characteristics of the data change over time.

Dana Angluin is a professor emeritus of computer science at Yale University. She is known for foundational work in computational learning theory and distributed computing.

<span class="mw-page-title-main">Ofer Dekel (researcher)</span>

Ofer Dekel is a computer science researcher in the Machine Learning Department of Microsoft Research. He obtained his PhD in Computer Science from the Hebrew University of Jerusalem and is an affiliate faculty at the Computer Science & Engineering department at the University of Washington.

<i>Minds and Machines</i> Academic journal

Minds and Machines is a peer-reviewed academic journal covering artificial intelligence, philosophy, and cognitive science.

Geoffrey I. Webb is Professor of Computer Science at Monash University, founder and director of Data Mining software development and consultancy company G. I. Webb and Associates, and former editor-in-chief of the journal Data Mining and Knowledge Discovery. Before joining Monash University he was on the faculty at Griffith University from 1986 to 1988 and then at Deakin University from 1988 to 2002.

<span class="mw-page-title-main">Kernel perceptron</span>

In machine learning, the kernel perceptron is a variant of the popular perceptron learning algorithm that can learn kernel machines, i.e. non-linear classifiers that employ a kernel function to compute the similarity of unseen samples to training samples. The algorithm was invented in 1964, making it the first kernel classification learner.

Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative and often recursive programs from incomplete specifications, such as input/output examples or constraints.

<span class="mw-page-title-main">Outline of machine learning</span> Overview of and topical guide to machine learning

The following outline is provided as an overview of and topical guide to machine learning. Machine learning is a subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.

Conformal prediction (CP) is a statistical technique for producing prediction sets without assumptions on the predictive algorithm (often a machine learning system) and only assuming exchangeability of the data. CP works by computing a nonconformity measure, often called a score function, on previously labeled data, and using these to create prediction sets on a new (unlabeled) test data point. A version of CP was first proposed in 1998 by Gammerman, Vovk, and Vapnik, and since, several variants of conformal prediction have been developed with different computational complexities, formal guarantees, and practical applications.

References

  1. "Editorial Board of the Kluwer Journal, Machine Learning: Resignation Letter". SIGIR Forum. 35 (2). 2001.
  2. Robin, Peek (1 December 2001). "Machine Learning's Editorial Board Divided". Information Today. 18 (11).