Typically, these approaches follow a machine learning approach, where large numbers of manually rated photographs are used to " teach " a computer about what visual properties are of relevance to aesthetic quality.

In statistics and machine learning it is called a log-linear model.

Examples include: pattern recognition, data mining, machine learning algorithms, and visualization.

Many modern machine learning methods are based on objectivist Bayesian principles.

Nonetheless, Bayesian methods are widely accepted and used, such as in the fields of machine learning and talent analytics.

Bayesian models, often drawn from machine learning, are also gaining popularity.

In addition, machine learning techniques can be used to detect new worms, by analyzing the behavior of the suspected computer.

Furthermore, cheminformatics uses even more empirical ( and computationally cheaper ) methods like machine learning based on physicochemical properties.

In order to achieve credible improvisation in particular style, machine improvisation uses machine learning and pattern matching algorithms to analyze existing musical examples.

There is a close connection between machine learning and compression: a system that predicts the posterior probabilities of a sequence given its entire history can be used for optimal data compression ( by using arithmetic coding on the output distribution ), while an optimal compressor can be used for prediction ( by finding the symbol that compresses best, given the previous history ).

* The Bupa liver data, used in several papers in the machine learning ( data mining ) literature.

It is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task.

Hank's grandson Spencer Greenberg is a machine learning scientist.

In computer science, specifically information retrieval and machine learning, the harmonic mean of the precision and the recall is often used as an aggregated performance score for the evaluation of algorithms and systems: the F-score ( or F-measure ).

Information theory is closely associated with a collection of pure and applied disciplines that have been investigated and reduced to engineering practice under a variety of rubrics throughout the world over the past half century or more: adaptive systems, anticipatory systems, artificial intelligence, complex systems, complexity science, cybernetics, informatics, machine learning, along with systems sciences of many descriptions.

* Kernel trick, in machine learning and statistics

In the domain of computer science, MIT faculty and researchers made fundamental contributions to cybernetics, artificial intelligence, computer languages, machine learning, robotics, and cryptography.

Minsky also built, in 1951, the first randomly wired neural network learning machine, SNARC.

Supervised learning is the machine learning task of inferring a function from labeled training data.

* Symbolic machine learning algorithms

* Subsymbolic machine learning algorithms

* Overfitting ( machine learning )

* mloss. org: a directory of open source machine learning software.

Although Acorn were able to shrink substantially the same functionality as the BBC into just one chip, manufacturing problems meant that very few machines were available for the Christmas period — to the extent that some shops reported eight presales for every delivered machine.

The complexity of solving the following problems with a human-assisted Turing machine is:

In computational complexity theory, BPP, which stands for bounded-error probabilistic polynomial time is the class of decision problems solvable by a probabilistic Turing machine in polynomial time, with an error probability of at most 1 / 3 for all instances.

In fact, BQP is low for PP, meaning that a PP machine achieves no benefit from being able to solve BQP problems instantly, an indication of the possible difference in power between these similar classes.

Vendors with security problems supply regular security updates ( see " Patch Tuesday "), and if these are installed to a machine then the majority of worms are unable to spread to it.

Since all problems in NP can be reduced to this problem it follows that for all problems in NP we can construct a non-deterministic Turing machine that decides the complement of the problem in polynomial time, i. e., NP is a subset of co-NP.

But as the halting problem is not generally solvable, and therefore calculating any but the first few bits of Chaitin's constant is not possible, this just reduces hard problems to impossible ones, much like trying to build an oracle machine for the halting problem would be.

The machine was used by other members of the University to solve real problems, and many early techniques were developed that are now included in operating systems.

The current version of BitchX, released in 2004, has security problems allowing remote IRC servers to execute arbitrary code on the client's machine ( CVE-2007-3360, CVE-2007-4584 ).

The machine fell in 1939 when Pendergast, riddled with health problems, pleaded guilty to tax evasion.

In an equivalent formal definition, NP is the set of decision problems where the " yes "- instances can be decided in polynomial time by a non-deterministic Turing machine.

Similarly, we have that NC is equivalent to the problems solvable on an alternating Turing machine restricted to at most two options at each step with space and alternations.

In complexity theory and computability theory, an oracle machine is an abstract machine used to study decision problems.

It can be visualized as a Turing machine with a black box, called an oracle, which is able to decide certain decision problems in a single operation.

For example, P < sup > SAT </ sup > is the class of problems solvable in polynomial time by a deterministic Turing machine with an oracle for the Boolean satisfiability problem.

For other points of view on online inputs to algorithms, see streaming algorithm ( focusing on the amount of memory needed to accurately represent past inputs ), dynamic algorithm ( focusing on the time complexity of maintaining solutions to problems with online inputs ) and online machine learning.

More formally, # P is the class of function problems of the form " compute ƒ ( x )," where ƒ is the number of accepting paths of a nondeterministic Turing machine running in polynomial time.

One consequence of Toda's theorem is that a polynomial-time machine with a # P oracle ( P < sup ># P </ sup >) can solve all problems in PH, the entire polynomial hierarchy.

SSH is important in cloud computing to solve connectivity problems, avoiding the security issues of exposing a cloud-based virtual machine directly on the Internet.

This machine was able to solve a wide range of effective problems in the 1940s, many related to atomic bomb design.

Most major belligerents attempted to solve the problems of complexity and security presented by using large codebooks for cryptography with the use of ciphering machines, the most well known being the German Enigma machine.

Other problems can affect the ability to start or control the machine remotely: hardware failure of the machine or network, failure of the BIOS settings battery ( the machine will halt when started before the network connection is made, displaying an error message and requiring a keypress ), loss of control of the machine due to software problems ( machine hang, termination of remote control or networking software, etc.