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Section: Application. In force control, a basic distinction can be made between applications with pronounced contact and applications with potential contact. We speak of pronounced contact when the contact of the machine with the environment or the workpiece is a central component of the task and is explicitly controll... | Wikipedia - Force control - Application | 324 | 1,809 | null |
Section: History. The first important work on force control was published in 1980 by John Kenneth Salisbury at Stanford University. In it, he describes a method for active stiffness control, a simple form of impedance control. However, the method does not yet allow a combination with motion control, but here force cont... | Wikipedia - Force control - History | 315 | 1,705 | null |
Section: Force measurement > Direct force measurement. The trivial approach to force control is the direct measurement of the occurring contact forces via force/torque sensors at the end effector of the machine or at the wrist of the industrial robot. Force/torque sensors measure the occurring forces by measuring the d... | Wikipedia - Force control - Force measurement > Direct force measurement | 306 | 1,627 | null |
Section: Force measurement > Six-axis force/torque sensor. In modern applications, so-called six-axis force/torque sensors are frequently used. These are mounted between the robot hand and the end effector and can record both forces and torques in all three spatial directions. For this purpose, they are equipped with s... | Wikipedia - Force control - Force measurement > Six-axis force/torque sensor | 254 | 1,309 | null |
Section: Force measurement > Force estimation. A cost-saving alternative to direct force measurement is force estimation (also known as "indirect force measurement"). This makes it possible to dispense with the use of force/torque sensors. In addition to cost savings, dispensing with these sensors has other advantages:... | Wikipedia - Force control - Force measurement > Force estimation | 229 | 1,149 | null |
Section: Force measurement > Separation of dynamic and static forces. During force measurement and force estimation, filtering of the sensor signals may be necessary. Numerous side effects and secondary forces can occur which do not correspond to the measurement of the contact force. This is especially true if a larger... | Wikipedia - Force control - Force measurement > Separation of dynamic and static forces | 191 | 985 | null |
Section: Control concepts > Impedance control. Impedance control, or compliance control, regulates the compliance of the system, i.e., the link between force and position upon object contact. Compliance is defined in the literature as a "measure of the robot's ability to counteract contact forces." There are passive an... | Wikipedia - Force control - Control concepts > Impedance control | 167 | 857 | null |
Section: Control concepts > Impedance control > Passive impedance control. Passive compliance control (also known as compliance control) does not require force measurement because there is no explicit force control. Instead, the manipulator and/or end effector is flexibly designed in a way that can minimize contact for... | Wikipedia - Force control - Control concepts > Impedance control > Passive impedance control | 217 | 1,162 | null |
Section: Control concepts > Impedance control > Active impedance control. Active compliance control refers to the control of the manipulator based on a deviation of the end effector. This is particularly suitable for guiding robots by an operator, for example as part of a teach-in process. Active compliance control is ... | Wikipedia - Force control - Control concepts > Impedance control > Active impedance control | 342 | 1,223 | null |
Section: Control concepts > Direct force control > Parallel force/position control. One possibility for force control is parallel force/position control. The control is designed as a cascade control and has an external force control loop and an internal position control loop. As shown in the following figure, a corresp... | Wikipedia - Force control - Control concepts > Direct force control > Parallel force/position control | 248 | 1,144 | null |
Section: Control concepts > Direct force control > Hybrid force/position control. An improvement over the above concepts is offered by hybrid force/position control, which works with two separate control systems and can also be used with hard, inflexible contact surfaces. In hybrid force/position control, the space is ... | Wikipedia - Force control - Control concepts > Direct force control > Hybrid force/position control | 299 | 1,590 | null |
Article: Formal concept analysis. In information science, formal concept analysis (FCA) is a principled way of deriving a concept hierarchy or formal ontology from a collection of objects and their properties. Each concept in the hierarchy represents the objects sharing some set of properties; and each sub-concept in t... | Wikipedia - Formal concept analysis - Summary | 154 | 812 | null |
Section: Overview and history. The original motivation of formal concept analysis was the search for real-world meaning of mathematical order theory. One such possibility of very general nature is that data tables can be transformed into algebraic structures called complete lattices, and that these can be utilized for ... | Wikipedia - Formal concept analysis - Overview and history | 315 | 1,542 | null |
Then the mathematical theory of formal concept analysis may be helpful, e.g., for decomposing the lattice into smaller pieces without information loss, or for embedding it into another structure that is easier to interpret. The theory in its present form goes back to the early 1980s and a research group led by Rudolf W... | Wikipedia - Formal concept analysis - Overview and history | 168 | 852 | null |
Section: Motivation and philosophical background. In his article "Restructuring Lattice Theory" (1982), initiating formal concept analysis as a mathematical discipline, Wille starts from a discontent with the current lattice theory and pure mathematics in general: The production of theoretical results—often achieved by... | Wikipedia - Formal concept analysis - Motivation and philosophical background | 341 | 1,839 | null |
Section: Example. The data in the example is taken from a semantic field study, where different kinds of bodies of water were systematically categorized by their attributes. For the purpose here it has been simplified. The data table represents a formal context, the line diagram next to it shows its concept lattice. Fo... | Wikipedia - Formal concept analysis - Example | 311 | 1,577 | null |
Section: Formal contexts and concepts. A formal context is a triple K = (G, M, I), where G is a set of objects, M is a set of attributes, and I ⊆ G × M is a binary relation called incidence that expresses which objects have which attributes. For subsets A ⊆ G of objects and subsets B ⊆ M of attributes, one defines two ... | Wikipedia - Formal concept analysis - Formal contexts and concepts | 316 | 1,129 | null |
With these derivation operators, Wille gave an elegant definition of a formal concept: a pair (A,B) is a formal concept of a context (G, M, I) provided that: A ⊆ G, B ⊆ M, A′ = B, and B′ = A. Equivalently and more intuitively, (A,B) is a formal concept precisely when: every object in A has every attribute in B, for eve... | Wikipedia - Formal concept analysis - Formal contexts and concepts | 291 | 1,253 | null |
Section: Concept lattice of a formal context. The concepts (Ai, Bi) of a context K can be (partially) ordered by the inclusion of extents, or, equivalently, by the dual inclusion of intents. An order ≤ on the concepts is defined as follows: for any two concepts (A1, B1) and (A2, B2) of K, we say that (A1, B1) ≤ (A2, B2... | Wikipedia - Formal concept analysis - Concept lattice of a formal context | 234 | 948 | null |
Section: Attribute values and negation. Real-world data is often given in the form of an object-attribute table, where the attributes have "values". Formal concept analysis handles such data by transforming them into the basic type of a ("one-valued") formal context. The method is called conceptual scaling. The negatio... | Wikipedia - Formal concept analysis - Attribute values and negation | 166 | 759 | null |
Section: Arrow relations. Formal concept analysis has elaborate mathematical foundations, making the field versatile. As a basic example we mention the arrow relations, which are simple and easy to compute, but very useful. They are defined as follows: For g ∈ G and m ∈ M let g ↗ m ⇔ (g, m) ∉ I and if m⊆n′ and m′ ≠ n′ ... | Wikipedia - Formal concept analysis - Arrow relations | 205 | 811 | null |
Section: Extensions of the theory. Triadic concept analysis replaces the binary incidence relation between objects and attributes by a ternary relation between objects, attributes, and conditions. An incidence ( g , m , c ) {\displaystyle (g,m,c)} then expresses that the object g has the attribute m under the condi... | Wikipedia - Formal concept analysis - Extensions of the theory | 348 | 1,437 | null |
The concept lattice equipped with the two additional operations Δ and 𝛁 is known as the concept algebra of a context. Concept algebras generalize power sets. Weak negation on a concept lattice L is a weak complementation, i.e. an order-reversing map Δ: L → L which satisfies the axioms xΔΔ ≤ x and (x⋀y) ⋁ (x⋀yΔ) = x. W... | Wikipedia - Formal concept analysis - Extensions of the theory | 162 | 640 | null |
Section: Extensions of the theory > Temporal concept analysis. Temporal concept analysis (TCA) is an extension of Formal Concept Analysis (FCA) aiming at a conceptual description of temporal phenomena. It provides animations in concept lattices obtained from data about changing objects. It offers a general way of under... | Wikipedia - Formal concept analysis - Extensions of the theory > Temporal concept analysis | 311 | 1,556 | null |
Section: Algorithms and tools. There are a number of simple and fast algorithms for generating formal concepts and for constructing and navigating concept lattices. For a survey, see Kuznetsov and Obiedkov or the book by Ganter and Obiedkov, where also some pseudo-code can be found. Since the number of formal concepts ... | Wikipedia - Formal concept analysis - Algorithms and tools | 181 | 923 | null |
Section: Related analytical techniques > Biclustering and multidimensional clustering. Given an object-attribute numerical data-table, the goal of biclustering is to group together some objects having similar values of some attributes. For example, in gene expression data, it is known that genes (objects) may share a c... | Wikipedia - Formal concept analysis - Related analytical techniques > Biclustering and multidimensional clustering | 337 | 1,589 | null |
object) or a pair of entities (e.g. attribute-condition), respectively. A bicluster of similar values in a numerical object-attribute data-table is usually defined as a pair consisting of an inclusion-maximal set of objects and an inclusion-maximal set of attributes having similar values for the objects. Such a pair ca... | Wikipedia - Formal concept analysis - Related analytical techniques > Biclustering and multidimensional clustering | 193 | 955 | null |
Section: Hands-on experience with formal concept analysis. The formal concept analysis can be used as a qualitative method for data analysis. Since the early beginnings of FCA in the early 1980s, the FCA research group at TU Darmstadt has gained experience from more than 200 projects using the FCA (as of 2005). Includi... | Wikipedia - Formal concept analysis - Hands-on experience with formal concept analysis | 162 | 825 | null |
Article: Generative artificial intelligence. Generative artificial intelligence (Generative AI, GenAI, or GAI) is a subfield of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. These models learn the underlying patterns and structures of their training data an... | Wikipedia - Generative artificial intelligence - Summary | 335 | 1,568 | null |
Section: History > Early history. The first example of an algorithmically generated media is likely the Markov chain. Markov chains have long been used to model natural languages since their development by Russian mathematician Andrey Markov in the early 20th century. Markov published his first paper on the topic in 19... | Wikipedia - Generative artificial intelligence - History > Early history | 244 | 1,296 | null |
Section: History > Generative neural networks (2014-2019). Since inception, the field of machine learning has used both discriminative models and generative models to model and predict data. Beginning in the late 2000s decade, the emergence of deep learning drove progress, and research in image classification, speech r... | Wikipedia - Generative artificial intelligence - History > Generative neural networks (2014-2019) | 316 | 1,561 | null |
Section: History > Generative AI boom (2020-). In March 2020, the release of 15.ai, a free web application created by an anonymous MIT researcher that could generate convincing character voices using minimal training data, marked one of the earliest popular use cases of generative AI. The platform is credited as the fi... | Wikipedia - Generative artificial intelligence - History > Generative AI boom (2020-) | 293 | 1,520 | null |
In March 2023, GPT-4's release represented another jump in generative AI capabilities. A team from Microsoft Research controversially argued that it "could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system." However, this assessment was contested by other... | Wikipedia - Generative artificial intelligence - History > Generative AI boom (2020-) | 338 | 1,601 | null |
Section: Applications. A generative AI system is constructed by applying unsupervised machine learning (invoking for instance neural network architectures such as generative adversarial networks (GANs), variation autoencoders (VAEs), transformers, or self-supervised machine learning trained on a dataset. The capabiliti... | Wikipedia - Generative artificial intelligence - Applications | 334 | 1,720 | null |
Section: Applications > Text and software code. Generative AI systems trained on words or word tokens include GPT-3, GPT-4, GPT-4o, LaMDA, LLaMA, BLOOM, Gemini and others (see List of large language models). They are capable of natural language processing, machine translation, and natural language generation and can be... | Wikipedia - Generative artificial intelligence - Applications > Text and software code | 198 | 949 | null |
Section: Applications > Audio. Generative AI can also be trained extensively on audio clips to produce natural-sounding speech synthesis and text-to-speech capabilities. An early pioneer in this field was 15.ai, launched in March 2020, which demonstrated the ability to clone character voices using as little as 15 secon... | Wikipedia - Generative artificial intelligence - Applications > Audio | 274 | 1,407 | null |
Section: Software and hardware. Generative AI models are used to power chatbot products such as ChatGPT, programming tools such as GitHub Copilot, text-to-image products such as Midjourney, and text-to-video products such as Runway Gen-2. Generative AI features have been integrated into a variety of existing commercial... | Wikipedia - Generative artificial intelligence - Software and hardware | 331 | 1,595 | null |
That forum is one of only two sources Andrej Karpathy trusts for language model benchmarks. Yann LeCun has advocated open-source models for their value to vertical applications and for improving AI safety. Language models with hundreds of billions of parameters, such as GPT-4 or PaLM, typically run on datacenter comput... | Wikipedia - Generative artificial intelligence - Software and hardware | 275 | 1,357 | null |
Section: Software and hardware > Generative models and training techniques > Generative adversarial networks. Generative adversarial networks (GANs) are an influential generative modeling technique. GANs consist of two neural networks—the generator and the discriminator—trained simultaneously in a competitive setting. ... | Wikipedia - Generative artificial intelligence - Software and hardware > Generative models and training techniques > Generative adversarial networks | 158 | 852 | null |
Section: Software and hardware > Generative models and training techniques > Variational autoencoders. Variational autoencoders (VAEs) are deep learning models that probabilistically encode data. They are typically used for tasks such as noise reduction from images, data compression, identifying unusual patterns, and f... | Wikipedia - Generative artificial intelligence - Software and hardware > Generative models and training techniques > Variational autoencoders | 248 | 1,235 | null |
Section: Software and hardware > Generative models and training techniques > Variational autoencoders > Transformers. Transformers became the foundation for many powerful generative models, most notably the generative pre-trained transformer (GPT) series developed by OpenAI. They marked a major shift in natural languag... | Wikipedia - Generative artificial intelligence - Software and hardware > Generative models and training techniques > Variational autoencoders > Transformers | 173 | 961 | null |
Section: Law and regulation. In the United States, a group of companies including OpenAI, Alphabet, and Meta signed a voluntary agreement with the Biden administration in July 2023 to watermark AI-generated content. In October 2023, Executive Order 14110 applied the Defense Production Act to require all US companies to... | Wikipedia - Generative artificial intelligence - Law and regulation | 188 | 1,015 | null |
Section: Law and regulation > Copyright > Training with copyrighted content. Generative AI systems such as ChatGPT and Midjourney are trained on large, publicly available datasets that include copyrighted works. AI developers have argued that such training is protected under fair use, while copyright holders have argue... | Wikipedia - Generative artificial intelligence - Law and regulation > Copyright > Training with copyrighted content | 202 | 1,021 | null |
Section: Law and regulation > Copyright > Copyright of AI-generated content. A separate question is whether AI-generated works can qualify for copyright protection. The United States Copyright Office has ruled that works created by artificial intelligence without any human input cannot be copyrighted, because they lack... | Wikipedia - Generative artificial intelligence - Law and regulation > Copyright > Copyright of AI-generated content | 278 | 1,444 | null |
Section: Concerns > Job losses. From the early days of the development of AI, there have been arguments put forward by ELIZA creator Joseph Weizenbaum and others about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative... | Wikipedia - Generative artificial intelligence - Concerns > Job losses | 292 | 1,560 | null |
Section: Concerns > Deepfakes. Deepfakes (a portmanteau of "deep learning" and "fake") are AI-generated media that take a person in an existing image or video and replace them with someone else's likeness using artificial neural networks. Deepfakes have garnered widespread attention and concerns for their uses in deepf... | Wikipedia - Generative artificial intelligence - Concerns > Deepfakes | 226 | 1,075 | null |
Section: Concerns > Deepfakes > Audio deepfakes. Instances of users abusing software to generate controversial statements in the vocal style of celebrities, public officials, and other famous individuals have raised ethical concerns over voice generation AI. In response, companies such as ElevenLabs have stated that th... | Wikipedia - Generative artificial intelligence - Concerns > Deepfakes > Audio deepfakes | 224 | 1,211 | null |
Section: Concerns > Cybercrime. Generative AI's ability to create realistic fake content has been exploited in numerous types of cybercrime, including phishing scams. Deepfake video and audio have been used to create disinformation and fraud. In 2020, former Google click fraud czar Shuman Ghosemajumder argued that once... | Wikipedia - Generative artificial intelligence - Concerns > Cybercrime | 223 | 1,114 | null |
Section: Concerns > Energy and environment. AI has a significant carbon footprint due to growing energy consumption from both training and usage. Scientists and journalists have expressed concerns about the environmental impact that the development and deployment of generative models are having: high CO2 emissions, lar... | Wikipedia - Generative artificial intelligence - Concerns > Energy and environment | 300 | 1,679 | null |
Section: Concerns > Content quality. The New York Times defines slop as analogous to spam: "shoddy or unwanted A.I. content in social media, art, books and ... in search results." Journalists have expressed concerns about the scale of low-quality generated content with respect to social media content moderation, the mo... | Wikipedia - Generative artificial intelligence - Concerns > Content quality | 326 | 1,653 | null |
The adoption of generative AI tools led to an explosion of AI-generated content across multiple domains. A study from University College London estimated that in 2023, more than 60,000 scholarly articles—over 1% of all publications—were likely written with LLM assistance. According to Stanford University's Institute fo... | Wikipedia - Generative artificial intelligence - Concerns > Content quality | 344 | 1,756 | null |
Section: Concerns > Misuse in journalism. In January 2023, Futurism.com broke the story that CNET had been using an undisclosed internal AI tool to write at least 77 of its stories; after the news broke, CNET posted corrections to 41 of the stories. In April 2023, the German tabloid Die Aktuelle published a fake AI-gen... | Wikipedia - Generative artificial intelligence - Concerns > Misuse in journalism | 334 | 1,676 | null |
Algorithmically generated anchors have also been used by allies of ISIS for their broadcasts. In 2023, Google reportedly pitched a tool to news outlets that claimed to "produce news stories" based on input data provided, such as "details of current events". Some news company executives who viewed the pitch described it... | Wikipedia - Generative artificial intelligence - Concerns > Misuse in journalism | 326 | 1,636 | null |
Meta AI, a chatbot based on Llama 3 which summarizes news stories, was noted by The Washington Post to copy sentences from those stories without direct attribution and to potentially further decrease the traffic of online news outlets. In response to potential pitfalls around the use and misuse of generative AI in jour... | Wikipedia - Generative artificial intelligence - Concerns > Misuse in journalism | 249 | 1,228 | null |
Article: Generative model. In statistical classification, two main approaches are called the generative approach and the discriminative approach. These compute classifiers by different approaches, differing in the degree of statistical modelling. Terminology is inconsistent, but three major types can be distinguished: ... | Wikipedia - Generative model - Summary | 346 | 1,604 | null |
Analogously, a classifier based on a generative model is a generative classifier, while a classifier based on a discriminative model is a discriminative classifier, though this term also refers to classifiers that are not based on a model. Standard examples of each, all of which are linear classifiers, are: generative ... | Wikipedia - Generative model - Summary | 298 | 1,322 | null |
Section: Definition. An alternative division defines these symmetrically as: a generative model is a model of the conditional probability of the observable X, given a target y, symbolically, P ( X ∣ Y = y ) {\displaystyle P(X\mid Y=y)} a discriminative model is a model of the conditional probability of the target Y, gi... | Wikipedia - Generative model - Definition | 339 | 1,488 | null |
Section: Definition > Relationships between models. In application to classification, the observable X is frequently a continuous variable, the target Y is generally a discrete variable consisting of a finite set of labels, and the conditional probability P ( Y ∣ X ) {\displaystyle P(Y\mid X)} can also be interpreted a... | Wikipedia - Generative model - Definition > Relationships between models | 312 | 1,225 | null |
{\displaystyle P(X,Y)=P(X\mid Y)P(Y).} Thus, while a model of the joint probability distribution is more informative than a model of the distribution of label (but without their relative frequencies), it is a relatively small step, hence these are not always distinguished. Given a model of the joint distribution, P ( X... | Wikipedia - Generative model - Definition > Relationships between models | 309 | 940 | null |
Given a model of the joint distribution, P ( X , Y ) {\displaystyle P(X,Y)} , the distribution of the individual variables can be computed as the marginal distributions P ( X ) = ∑ y P ( X , Y = y ) {\displaystyle P(X)=\sum _{y}P(X,Y=y)} and P ( Y ) = ∫ x P ( Y , X = x ) {\displaystyle P(Y)=\int _{x}P(Y,X=x)} (consider... | Wikipedia - Generative model - Definition > Relationships between models | 324 | 968 | null |
Given a model of one conditional probability, and estimated probability distributions for the variables X and Y, denoted P ( X ) {\displaystyle P(X)} and P ( Y ) {\displaystyle P(Y)} , one can estimate the opposite conditional probability using Bayes' rule: P ( X ∣ Y ) P ( Y ) = P ( Y ∣ X ) P ( X ) . {\displaystyle P(X... | Wikipedia - Generative model - Definition > Relationships between models | 324 | 968 | null |
Section: Contrast with discriminative classifiers. A generative algorithm models how the data was generated in order to categorize a signal. It asks the question: based on my generation assumptions, which category is most likely to generate this signal? A discriminative algorithm does not care about how the data was ge... | Wikipedia - Generative model - Contrast with discriminative classifiers | 309 | 1,392 | null |
Section: Deep generative models. With the rise of deep learning, a new family of methods, called deep generative models (DGMs), is formed through the combination of generative models and deep neural networks. An increase in the scale of the neural networks is typically accompanied by an increase in the scale of the tra... | Wikipedia - Generative model - Deep generative models | 203 | 909 | null |
Section: Types > Generative models. Types of generative models are: Gaussian mixture model (and other types of mixture model) Hidden Markov model Probabilistic context-free grammar Bayesian network (e.g. Naive bayes, Autoregressive model) Averaged one-dependence estimators Latent Dirichlet allocation Boltzmann machine ... | Wikipedia - Generative model - Types > Generative models | 253 | 1,227 | null |
Section: Examples > Simple example. Suppose the input data is x ∈ { 1 , 2 } {\displaystyle x\in \{1,2\}} , the set of labels for x {\displaystyle x} is y ∈ { 0 , 1 } {\displaystyle y\in \{0,1\}} , and there are the following 4 data points: ( x , y ) = { ( 1 , 0 ) , ( 1 , 1 ) , ( 2 , 0 ) , ( 2 , 1 ) } {\displaystyle (x,... | Wikipedia - Generative model - Examples > Simple example | 208 | 562 | null |
Article: Geometric feature learning. Geometric feature learning is a technique combining machine learning and computer vision to solve visual tasks. The main goal of this method is to find a set of representative features of geometric form to represent an object by collecting geometric features from images and learning... | Wikipedia - Geometric feature learning - Summary | 210 | 1,237 | null |
Section: Introduction > Geometric features. Primitive features Corners: Corners are a very simple but significant feature of objects. Especially, Complex objects usually have different corner features with each other. Corners of an object can be extracted through Corner detection. Cho and Dunn used a different way to d... | Wikipedia - Geometric feature learning - Introduction > Geometric features | 231 | 1,242 | null |
Ridges detection method-see ridge detection salient points-see Kadir–Brady saliency detector image texture Compound features Geometric composition Geometric component feature is a combination of several primitive features and it always consists more than 2 primitive features like edges, corners or blobs. Extracting geo... | Wikipedia - Geometric feature learning - Introduction > Geometric features | 334 | 1,022 | null |
Extracting geometric feature vector at location x can be computed according to the reference point, which is shown below: x i = x i − 1 + σ i − 1 d i [ cos ( θ i − 1 + ϕ i ) sin ( θ i − 1 + ϕ i ) ] {\displaystyle \textstyle \ x_{i}=x_{i-1}+\sigma _{i-1}d_{i}{\begin{bmatrix}\cos(\theta _{i-1}+\phi _{i})\\\sin(\theta... | Wikipedia - Geometric feature learning - Introduction > Geometric features | 342 | 1,028 | null |
So using below equation to maximise the feature f m a x {\displaystyle \textstyle \ f_{max}} I m a x = m a x f m a x C I ( C , F f ) {\displaystyle \textstyle \ I_{max}={\underset {f}{max}}{\underset {C}{max}}I(C,F_{f})} I ( C , F f ) = − ∑ C ∑ F f B E L ( F f , C ) log B E L ( C , F f ) B E L ( F f ) B E L ( C ) {\d... | Wikipedia - Geometric feature learning - Feature learning algorithm | 344 | 737 | null |
I}{max}}f_{f_{(p)}}(x)} Where f f ( p ) ( x ) {\displaystyle \textstyle f_{f_{(p)}}(x)} is defined as f f ( p ) ( I ) = m a x { 0 , f ( p ) T ) f ( x ) ‖ f ( p ) ‖ ‖ f ( x ) ‖ } {\displaystyle \textstyle f_{f_{(p)}}(I)=max\left\{0,{\frac {f(p)^{T})f(x)}{\left\|f(p)\right\|\left\|f(x)\right\|}}\right\}} evaluation After... | Wikipedia - Geometric feature learning - Feature learning algorithm | 261 | 756 | null |
If the recognition failed, the feature nodes should be maximise their distinctive power which is defined by the Kolmogorov-Smirno distance (KSD). K S D a , b ( X ) = m a x α | c d f ( α | a ) − c d f ( α | b ) | {\displaystyle \textstyle KSD_{a,b}(X)={\underset {\alpha }{max}}\left|cdf(\alpha |a)-cdf(\alpha |b)\right|}... | Wikipedia - Geometric feature learning - Feature learning algorithm | 275 | 1,150 | null |
Section: PAC model based feature learning algorithm > Learning framework. The probably approximately correct (PAC) model was applied by D. Roth (2002) to solve computer vision problem by developing a distribution-free learning theory based on this model. This theory heavily relied on the development of feature-efficien... | Wikipedia - Geometric feature learning - PAC model based feature learning algorithm > Learning framework | 194 | 1,021 | null |
Section: PAC model based feature learning algorithm > Evaluation framework. After learning features, there should be some evaluation algorithms to evaluate the learning algorithms. D. Roth applied two learning algorithms: 1.Sparse Network of Winnows(SNoW) system SNoW-Train Initial step: initial the set of features F t ... | Wikipedia - Geometric feature learning - PAC model based feature learning algorithm > Evaluation framework | 293 | 1,083 | null |
\theta_{t} is the threshold for the target not t. Update weight according to the result of evaluation. There are two cases: predicted positive on negative example ( ∑ i ∈ e w i t > θ t {\displaystyle \textstyle {\underset {i\in e}{\sum }}w_{i}^{t}>\theta _{t}} and targets are not in the list of active features) and pre... | Wikipedia - Geometric feature learning - PAC model based feature learning algorithm > Evaluation framework | 323 | 1,055 | null |
support vector machines The main purpose of SVM is to find a hyperplane to separate the set of samples ( x i , y i ) {\displaystyle \textstyle (x_{i},y_{i})} where x i {\displaystyle \textstyle x_{i}} is an input vector which is a selection of features x ∈ R N {\displaystyle \textstyle x\in R^{N}} and y i {\displaystyl... | Wikipedia - Geometric feature learning - PAC model based feature learning algorithm > Evaluation framework | 366 | 932 | null |
Section: A. A* search Pronounced "A-star". A graph traversal and pathfinding algorithm which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. abductive logic programming (ALP) A high-level knowledge-representation framework that can be used to solve problems declar... | Wikipedia - Glossary of artificial intelligence - A | 335 | 1,741 | null |
abstraction The process of removing physical, spatial, or temporal details or attributes in the study of objects or systems in order to more closely attend to other details of interest accelerating change A perceived increase in the rate of technological change throughout history, which may suggest faster and more prof... | Wikipedia - Glossary of artificial intelligence - A | 350 | 1,980 | null |
The technique was developed in the early 1990s. Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework. Its inference system corresponds to a set of fuzzy IF–THEN rules that have learning capability to approximate nonlinear functions. ... | Wikipedia - Glossary of artificial intelligence - A | 341 | 1,877 | null |
AI-complete In the field of artificial intelligence, the most difficult problems are informally known as AI-complete or AI-hard, implying that the difficulty of these computational problems is equivalent to that of solving the central artificial intelligence problem—making computers as intelligent as people, or strong ... | Wikipedia - Glossary of artificial intelligence - A | 348 | 1,889 | null |
analysis of algorithms The determination of the computational complexity of algorithms, that is the amount of time, storage and/or other resources necessary to execute them. Usually, this involves determining a function that relates the length of an algorithm's input to the number of steps it takes (its time complexity... | Wikipedia - Glossary of artificial intelligence - A | 326 | 1,738 | null |
approximate string matching Also fuzzy string searching. The technique of finding strings that match a pattern approximately (rather than exactly). The problem of approximate string matching is typically divided into two sub-problems: finding approximate substring matches inside a given string and finding dictionary st... | Wikipedia - Glossary of artificial intelligence - A | 350 | 1,964 | null |
In computer science, AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans ... | Wikipedia - Glossary of artificial intelligence - A | 349 | 1,882 | null |
attributional calculus A logic and representation system defined by Ryszard S. Michalski. It combines elements of predicate logic, propositional calculus, and multi-valued logic. Attributional calculus provides a formal language for natural induction, an inductive learning process whose results are in forms natural to ... | Wikipedia - Glossary of artificial intelligence - A | 337 | 1,693 | null |
Planning is also related to decision theory. automated reasoning An area of computer science and mathematical logic dedicated to understanding different aspects of reasoning. The study of automated reasoning helps produce computer programs that allow computers to reason completely, or nearly completely, automatically. ... | Wikipedia - Glossary of artificial intelligence - A | 217 | 1,307 | null |
Section: B. backpropagation A method used in artificial neural networks to calculate a gradient that is needed in the calculation of the weights to be used in the network. Backpropagation is shorthand for "the backward propagation of errors", since an error is computed at the output and distributed backwards throughout... | Wikipedia - Glossary of artificial intelligence - B | 324 | 1,629 | null |
The bag-of-words model is commonly used in methods of document classification where the (frequency of) occurrence of each word is used as a feature for training a classifier. bag-of-words model in computer vision In computer vision, the bag-of-words model (BoW model) can be applied to image classification, by treating ... | Wikipedia - Glossary of artificial intelligence - B | 334 | 1,718 | null |
The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies. behavior informatics (BI) The informatics of behaviors so as to obtain behavior intelligence and behavior insights. behavior tree (BT) A mathematical model of plan execution used in computer science, robotics, contro... | Wikipedia - Glossary of artificial intelligence - B | 318 | 1,687 | null |
A third activity, creating the plans in the first place (planning), is not within the scope of the model, and is left to the system designer and programmer. bias–variance tradeoff In statistics and machine learning, the bias–variance tradeoff is the property of a set of predictive models whereby models with a lower bia... | Wikipedia - Glossary of artificial intelligence - B | 299 | 1,449 | null |
Some authors allow the binary tree to be the empty set as well. blackboard system An artificial intelligence approach based on the blackboard architectural model, where a common knowledge base, the "blackboard", is iteratively updated by a diverse group of specialist knowledge sources, starting with a problem specifica... | Wikipedia - Glossary of artificial intelligence - B | 338 | 1,582 | null |
In contrast, "a AND NOT a" is unsatisfiable. boosting A machine learning ensemble metaheuristic for primarily reducing bias (as opposed to variance), by training models sequentially, each one correcting the errors of its predecessor. bootstrap aggregating Also bagging or bootstrapping. A machine learning ensemble metah... | Wikipedia - Glossary of artificial intelligence - B | 281 | 1,464 | null |
Section: C. capsule neural network (CapsNet) A machine learning system that is a type of artificial neural network (ANN) that can be used to better model hierarchical relationships. The approach is an attempt to more closely mimic biological neural organization. case-based reasoning (CBR) Broadly construed, the process... | Wikipedia - Glossary of artificial intelligence - C | 347 | 1,842 | null |
The task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, includin... | Wikipedia - Glossary of artificial intelligence - C | 345 | 1,800 | null |
cognitive science The interdisciplinary scientific study of the mind and its processes. combinatorial optimization In Operations Research, applied mathematics and theoretical computer science, combinatorial optimization is a topic that consists of finding an optimal object from a finite set of objects. committee machin... | Wikipedia - Glossary of artificial intelligence - C | 336 | 2,038 | null |
computational humor A branch of computational linguistics and artificial intelligence which uses computers in humor research. computational intelligence (CI) Usually refers to the ability of a computer to learn a specific task from data or experimental observation. computational learning theory In computer science, com... | Wikipedia - Glossary of artificial intelligence - C | 340 | 2,178 | null |
Extending Computer-Aided Design (CAD), automated design and computer-automated design are concerned with a broader range of applications, such as automotive engineering, civil engineering, composite material design, control engineering, dynamic system identification and optimization, financial systems, industrial equip... | Wikipedia - Glossary of artificial intelligence - C | 295 | 1,710 | null |
connectionism An approach in the fields of cognitive science, that hopes to explain mental phenomena using artificial neural networks. consistent heuristic In the study of path-finding problems in artificial intelligence, a heuristic function is said to be consistent, or monotone, if its estimate is always less than or... | Wikipedia - Glossary of artificial intelligence - C | 350 | 1,666 | null |
A language whose phonology, grammar, and vocabulary are consciously devised, instead of having developed naturally. Constructed languages may also be referred to as artificial, planned, or invented languages. control theory In control systems engineering is a subfield of mathematics that deals with the control of conti... | Wikipedia - Glossary of artificial intelligence - C | 288 | 1,569 | null |
Section: D. Darkforest A computer go program developed by Facebook, based on deep learning techniques using a convolutional neural network. Its updated version Darkfores2 combines the techniques of its predecessor with Monte Carlo tree search. The MCTS effectively takes tree search methods commonly seen in computer che... | Wikipedia - Glossary of artificial intelligence - D | 322 | 1,755 | null |
data mining The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. data science An interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various f... | Wikipedia - Glossary of artificial intelligence - D | 330 | 1,664 | null |
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