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AlaaElhilo/Wikipedia_ComputerScience | From the technology perspective, speech recognition has a long history with several waves of major innovations. Most recently, the field has benefited from advances in deep learning and big data. The advances are evidenced not only by the surge of academic papers published in the field, but more importantly by the worl... |
AlaaElhilo/Wikipedia_ComputerScience | The key areas of growth were: vocabulary size, speaker independence, and processing speed. |
AlaaElhilo/Wikipedia_ComputerScience | Raj Reddy was the first person to take on continuous speech recognition as a graduate student at Stanford University in the late 1960s. Previous systems required users to pause after each word. Reddy's system issued spoken commands for playing chess. |
AlaaElhilo/Wikipedia_ComputerScience | Around this time Soviet researchers invented the dynamic time warping algorithm and used it to create a recognizer capable of operating on a 200-word vocabulary. DTW processed speech by dividing it into short frames, e.g. 10ms segments, and processing each frame as a single unit. Although DTW would be superseded by la... |
AlaaElhilo/Wikipedia_ComputerScience | During the late 1960s Leonard Baum developed the mathematics of Markov chains at the Institute for Defense Analysis. A decade later, at CMU, Raj Reddy's students James Baker and Janet M. Baker began using the hidden Markov model for speech recognition. James Baker had learned about HMMs from a summer job at the Instit... |
AlaaElhilo/Wikipedia_ComputerScience | The 1980s also saw the introduction of the n-gram language model. |
AlaaElhilo/Wikipedia_ComputerScience | Much of the progress in the field is owed to the rapidly increasing capabilities of computers. At the end of the DARPA program in 1976, the best computer available to researchers was the PDP-10 with 4 MB ram. It could take up to 100 minutes to decode just 30 seconds of speech. |
AlaaElhilo/Wikipedia_ComputerScience | Two practical products were: |
AlaaElhilo/Wikipedia_ComputerScience | By this point, the vocabulary of the typical commercial speech recognition system was larger than the average human vocabulary. Raj Reddy's former student, Xuedong Huang, developed the Sphinx-II system at CMU. The Sphinx-II system was the first to do speaker-independent, large vocabulary, continuous speech recognition ... |
AlaaElhilo/Wikipedia_ComputerScience | Lernout & Hauspie, a Belgium-based speech recognition company, acquired several other companies, including Kurzweil Applied Intelligence in 1997 and Dragon Systems in 2000. The L&H speech technology was used in the Windows XP operating system. L&H was an industry leader until an accounting scandal brought an end to the... |
AlaaElhilo/Wikipedia_ComputerScience | In the 2000s DARPA sponsored two speech recognition programs: Effective Affordable Reusable Speech-to-Text in 2002 and Global Autonomous Language Exploitation . Four teams participated in the EARS program: IBM, a team led by BBN with LIMSI and Univ. of Pittsburgh, Cambridge University, and a team composed of ICSI, SRI... |
AlaaElhilo/Wikipedia_ComputerScience | In the United States, the National Security Agency has made use of a type of speech recognition for keyword spotting since at least 2006. This technology allows analysts to search through large volumes of recorded conversations and isolate mentions of keywords. Recordings can be indexed and analysts can run queries ove... |
AlaaElhilo/Wikipedia_ComputerScience | By early 2010s speech recognition, also called voice recognition was clearly differentiated from speaker recognition, and speaker independence was considered a major breakthrough. Until then, systems required a "training" period. A 1987 ad for a doll had carried the tagline "Finally, the doll that understands you." – ... |
AlaaElhilo/Wikipedia_ComputerScience | In 2017, Microsoft researchers reached a historical human parity milestone of transcribing conversational telephony speech on the widely benchmarked Switchboard task. Multiple deep learning models were used to optimize speech recognition accuracy. The speech recognition word error rate was reported to be as low as 4 pr... |
AlaaElhilo/Wikipedia_ComputerScience | Both acoustic modeling and language modeling are important parts of modern statistically based speech recognition algorithms. Hidden Markov models are widely used in many systems. Language modeling is also used in many other natural language processing applications such as document classification or statistical machin... |
AlaaElhilo/Wikipedia_ComputerScience | Modern general-purpose speech recognition systems are based on hidden Markov models. These are statistical models that output a sequence of symbols or quantities. HMMs are used in speech recognition because a speech signal can be viewed as a piecewise stationary signal or a short-time stationary signal. In a short time... |
AlaaElhilo/Wikipedia_ComputerScience | Another reason why HMMs are popular is that they can be trained automatically and are simple and computationally feasible to use. In speech recognition, the hidden Markov model would output a sequence of n-dimensional real-valued vectors , outputting one of these every 10 milliseconds. The vectors would consist of ceps... |
AlaaElhilo/Wikipedia_ComputerScience | Described above are the core elements of the most common, HMM-based approach to speech recognition. Modern speech recognition systems use various combinations of a number of standard techniques in order to improve results over the basic approach described above. A typical large-vocabulary system would need context depe... |
AlaaElhilo/Wikipedia_ComputerScience | Decoding of the speech would probably use the Viterbi algorithm to find the best path, and here there is a choice between dynamically creating a combination hidden Markov model, which includes both the acoustic and language model information and combining it statically beforehand . |
AlaaElhilo/Wikipedia_ComputerScience | A possible improvement to decoding is to keep a set of good candidates instead of just keeping the best candidate, and to use a better scoring function to rate these good candidates so that we may pick the best one according to this refined score. The set of candidates can be kept either as a list or as a subset of t... |
AlaaElhilo/Wikipedia_ComputerScience | Dynamic time warping is an approach that was historically used for speech recognition but has now largely been displaced by the more successful HMM-based approach. |
AlaaElhilo/Wikipedia_ComputerScience | Dynamic time warping is an algorithm for measuring similarity between two sequences that may vary in time or speed. For instance, similarities in walking patterns would be detected, even if in one video the person was walking slowly and if in another he or she were walking more quickly, or even if there were accelerati... |
AlaaElhilo/Wikipedia_ComputerScience | A well-known application has been automatic speech recognition, to cope with different speaking speeds. In general, it is a method that allows a computer to find an optimal match between two given sequences with certain restrictions. That is, the sequences are "warped" non-linearly to match each other. This sequence a... |
AlaaElhilo/Wikipedia_ComputerScience | Neural networks emerged as an attractive acoustic modeling approach in ASR in the late 1980s. Since then, neural networks have been used in many aspects of speech recognition such as phoneme classification, phoneme classification through multi-objective evolutionary algorithms, isolated word recognition, audiovisual sp... |
AlaaElhilo/Wikipedia_ComputerScience | Neural networks make fewer explicit assumptions about feature statistical properties than HMMs and have several qualities making them attractive recognition models for speech recognition. When used to estimate the probabilities of a speech feature segment, neural networks allow discriminative training in a natural and ... |
AlaaElhilo/Wikipedia_ComputerScience | One approach to this limitation was to use neural networks as a pre-processing, feature transformation or dimensionality reduction, step prior to HMM based recognition. However, more recently, LSTM and related recurrent neural networks , Time Delay Neural Networks, and transformers have demonstrated improved performanc... |
AlaaElhilo/Wikipedia_ComputerScience | Deep neural networks and denoising autoencoders are also under investigation. A deep feedforward neural network is an artificial neural network with multiple hidden layers of units between the input and output layers. Similar to shallow neural networks, DNNs can model complex non-linear relationships. DNN architecture... |
AlaaElhilo/Wikipedia_ComputerScience | Since 2014, there has been much research interest in "end-to-end" ASR. Traditional phonetic-based approaches required separate components and training for the pronunciation, acoustic, and language model. End-to-end models jointly learn all the components of the speech recognizer. This is valuable since it simplifies t... |
AlaaElhilo/Wikipedia_ComputerScience | The first attempt at end-to-end ASR was with Connectionist Temporal Classification -based systems introduced by Alex Graves of Google DeepMind and Navdeep Jaitly of the University of Toronto in 2014. The model consisted of recurrent neural networks and a CTC layer. Jointly, the RNN-CTC model learns the pronunciation an... |
AlaaElhilo/Wikipedia_ComputerScience | An alternative approach to CTC-based models are attention-based models. Attention-based ASR models were introduced simultaneously by Chan et al. of Carnegie Mellon University and Google Brain and Bahdanau et al. of the University of Montreal in 2016. The model named "Listen, Attend and Spell" , literally "listens" to t... |
AlaaElhilo/Wikipedia_ComputerScience | Typically a manual control input, for example by means of a finger control on the steering-wheel, enables the speech recognition system and this is signaled to the driver by an audio prompt. Following the audio prompt, the system has a "listening window" during which it may accept a speech input for recognition. |
AlaaElhilo/Wikipedia_ComputerScience | Simple voice commands may be used to initiate phone calls, select radio stations or play music from a compatible smartphone, MP3 player or music-loaded flash drive. Voice recognition capabilities vary between car make and model. Some of the most recent car models offer natural-language speech recognition in place of a ... |
AlaaElhilo/Wikipedia_ComputerScience | Automatic pronunciation assessment is the use of speech recognition to verify the correctness of pronounced speech, as distinguished from manual assessment by an instructor or proctor. Also called speech verification, pronunciation evaluation, and pronunciation scoring, the main application of this technology is comput... |
AlaaElhilo/Wikipedia_ComputerScience | Assessing authentic listener intelligibility is essential for avoiding inaccuracies from accent bias, especially in high-stakes assessments; from words with multiple correct pronunciations; and from phoneme coding errors in machine-readable pronunciation dictionaries. In 2022, researchers found that some newer speech t... |
AlaaElhilo/Wikipedia_ComputerScience | In the health care sector, speech recognition can be implemented in front-end or back-end of the medical documentation process. Front-end speech recognition is where the provider dictates into a speech-recognition engine, the recognized words are displayed as they are spoken, and the dictator is responsible for editing... |
AlaaElhilo/Wikipedia_ComputerScience | One of the major issues relating to the use of speech recognition in healthcare is that the American Recovery and Reinvestment Act of 2009 provides for substantial financial benefits to physicians who utilize an EMR according to "Meaningful Use" standards. These standards require that a substantial amount of data be m... |
AlaaElhilo/Wikipedia_ComputerScience | A more significant issue is that most EHRs have not been expressly tailored to take advantage of voice-recognition capabilities. A large part of the clinician's interaction with the EHR involves navigation through the user interface using menus, and tab/button clicks, and is heavily dependent on keyboard and mouse: voi... |
AlaaElhilo/Wikipedia_ComputerScience | Prolonged use of speech recognition software in conjunction with word processors has shown benefits to short-term-memory restrengthening in brain AVM patients who have been treated with resection. Further research needs to be conducted to determine cognitive benefits for individuals whose AVMs have been treated using r... |
AlaaElhilo/Wikipedia_ComputerScience | Substantial efforts have been devoted in the last decade to the test and evaluation of speech recognition in fighter aircraft. Of particular note have been the US program in speech recognition for the Advanced Fighter Technology Integration /F-16 aircraft , the program in France for Mirage aircraft, and other programs ... |
AlaaElhilo/Wikipedia_ComputerScience | Working with Swedish pilots flying in the JAS-39 Gripen cockpit, Englund found recognition deteriorated with increasing g-loads. The report also concluded that adaptation greatly improved the results in all cases and that the introduction of models for breathing was shown to improve recognition scores significantly. C... |
AlaaElhilo/Wikipedia_ComputerScience | The Eurofighter Typhoon, currently in service with the UK RAF, employs a speaker-dependent system, requiring each pilot to create a template. The system is not used for any safety-critical or weapon-critical tasks, such as weapon release or lowering of the undercarriage, but is used for a wide range of other cockpit fu... |
AlaaElhilo/Wikipedia_ComputerScience | Speaker-independent systems are also being developed and are under test for the F35 Lightning II and the Alenia Aermacchi M-346 Master lead-in fighter trainer. These systems have produced word accuracy scores in excess of 98%. |
AlaaElhilo/Wikipedia_ComputerScience | The problems of achieving high recognition accuracy under stress and noise are particularly relevant in the helicopter environment as well as in the jet fighter environment. The acoustic noise problem is actually more severe in the helicopter environment, not only because of the high noise levels but also because the h... |
AlaaElhilo/Wikipedia_ComputerScience | As in fighter applications, the overriding issue for voice in helicopters is the impact on pilot effectiveness. Encouraging results are reported for the AVRADA tests, although these represent only a feasibility demonstration in a test environment. Much remains to be done both in speech recognition and in overall speech... |
AlaaElhilo/Wikipedia_ComputerScience | Training for air traffic controllers represents an excellent application for speech recognition systems. Many ATC training systems currently require a person to act as a "pseudo-pilot", engaging in a voice dialog with the trainee controller, which simulates the dialog that the controller would have to conduct with pil... |
AlaaElhilo/Wikipedia_ComputerScience | The USAF, USMC, US Army, US Navy, and FAA as well as a number of international ATC training organizations such as the Royal Australian Air Force and Civil Aviation Authorities in Italy, Brazil, and Canada are currently using ATC simulators with speech recognition from a number of different vendors. |
AlaaElhilo/Wikipedia_ComputerScience | ASR is now commonplace in the field of telephony and is becoming more widespread in the field of computer gaming and simulation. In telephony systems, ASR is now being predominantly used in contact centers by integrating it with IVR systems. Despite the high level of integration with word processing in general personal... |
AlaaElhilo/Wikipedia_ComputerScience | The improvement of mobile processor speeds has made speech recognition practical in smartphones. Speech is used mostly as a part of a user interface, for creating predefined or custom speech commands. |
AlaaElhilo/Wikipedia_ComputerScience | People with disabilities can benefit from speech recognition programs. For individuals that are Deaf or Hard of Hearing, speech recognition software is used to automatically generate a closed-captioning of conversations such as discussions in conference rooms, classroom lectures, and/or religious services. |
AlaaElhilo/Wikipedia_ComputerScience | Students who are blind or have very low vision can benefit from using the technology to convey words and then hear the computer recite them, as well as use a computer by commanding with their voice, instead of having to look at the screen and keyboard. |
AlaaElhilo/Wikipedia_ComputerScience | Students who are physically disabled have a Repetitive strain injury/other injuries to the upper extremities can be relieved from having to worry about handwriting, typing, or working with scribe on school assignments by using speech-to-text programs. They can also utilize speech recognition technology to enjoy searchi... |
AlaaElhilo/Wikipedia_ComputerScience | Speech recognition can allow students with learning disabilities to become better writers. By saying the words aloud, they can increase the fluidity of their writing, and be alleviated of concerns regarding spelling, punctuation, and other mechanics of writing. Also, see Learning disability. |
AlaaElhilo/Wikipedia_ComputerScience | The use of voice recognition software, in conjunction with a digital audio recorder and a personal computer running word-processing software has proven to be positive for restoring damaged short-term memory capacity, in stroke and craniotomy individuals. |
AlaaElhilo/Wikipedia_ComputerScience | Speech recognition is also very useful for people who have difficulty using their hands, ranging from mild repetitive stress injuries to involve disabilities that preclude using conventional computer input devices. In fact, people who used the keyboard a lot and developed RSI became an urgent early market for speech re... |
AlaaElhilo/Wikipedia_ComputerScience | This type of technology can help those with dyslexia but other disabilities are still in question. The effectiveness of the product is the problem that is hindering it from being effective. Although a kid may be able to say a word depending on how clear they say it the technology may think they are saying another word ... |
AlaaElhilo/Wikipedia_ComputerScience | The performance of speech recognition systems is usually evaluated in terms of accuracy and speed. Accuracy is usually rated with word error rate , whereas speed is measured with the real time factor. Other measures of accuracy include Single Word Error Rate and Command Success Rate . |
AlaaElhilo/Wikipedia_ComputerScience | Speech recognition by machine is a very complex problem, however. Vocalizations vary in terms of accent, pronunciation, articulation, roughness, nasality, pitch, volume, and speed. Speech is distorted by a background noise and echoes, electrical characteristics. Accuracy of speech recognition may vary with the followin... |
AlaaElhilo/Wikipedia_ComputerScience | As mentioned earlier in this article, the accuracy of speech recognition may vary depending on the following factors: |
AlaaElhilo/Wikipedia_ComputerScience | With discontinuous speech full sentences separated by silence are used, therefore it becomes easier to recognize the speech as well as with isolated speech.
With continuous speech naturally spoken sentences are used, therefore it becomes harder to recognize the speech, different from both isolated and discontinuous s... |
AlaaElhilo/Wikipedia_ComputerScience | Constraints are often represented by grammar. |
AlaaElhilo/Wikipedia_ComputerScience | Speech recognition is a multi-leveled pattern recognition task. |
AlaaElhilo/Wikipedia_ComputerScience | e.g. Known word pronunciations or legal word sequences, which can compensate for errors or uncertainties at a lower level; |
AlaaElhilo/Wikipedia_ComputerScience | For telephone speech the sampling rate is 8000 samples per second; |
AlaaElhilo/Wikipedia_ComputerScience | computed every 10 ms, with one 10 ms section called a frame; |
AlaaElhilo/Wikipedia_ComputerScience | Analysis of four-step neural network approaches can be explained by further information. Sound is produced by air vibration, which we register by ears, but machines by receivers. Basic sound creates a wave which has two descriptions: amplitude , and frequency .
Accuracy can be computed with the help of word error rate... |
AlaaElhilo/Wikipedia_ComputerScience | The formula to compute the word error rate is: |
AlaaElhilo/Wikipedia_ComputerScience | W
E
R
=
n
{\displaystyle WER={ \over n}} |
AlaaElhilo/Wikipedia_ComputerScience | where s is the number of substitutions, d is the number of deletions, i is the number of insertions, and n is the number of word references. |
AlaaElhilo/Wikipedia_ComputerScience | While computing, the word recognition rate is used. The formula is: |
AlaaElhilo/Wikipedia_ComputerScience | where h is the number of correctly recognized words: |
AlaaElhilo/Wikipedia_ComputerScience | Speech recognition can become a means of attack, theft, or accidental operation. For example, activation words like "Alexa" spoken in an audio or video broadcast can cause devices in homes and offices to start listening for input inappropriately, or possibly take an unwanted action. Voice-controlled devices are also ac... |
AlaaElhilo/Wikipedia_ComputerScience | Two attacks have been demonstrated that use artificial sounds. One transmits ultrasound and attempt to send commands without nearby people noticing. The other adds small, inaudible distortions to other speech or music that are specially crafted to confuse the specific speech recognition system into recognizing music as... |
AlaaElhilo/Wikipedia_ComputerScience | Popular speech recognition conferences held each year or two include SpeechTEK and SpeechTEK Europe, ICASSP, Interspeech/Eurospeech, and the IEEE ASRU. Conferences in the field of natural language processing, such as ACL, NAACL, EMNLP, and HLT, are beginning to include papers on speech processing. Important journals in... |
AlaaElhilo/Wikipedia_ComputerScience | Books like "Fundamentals of Speech Recognition" by Lawrence Rabiner can be useful to acquire basic knowledge but may not be fully up to date . Another good source can be "Statistical Methods for Speech Recognition" by Frederick Jelinek and "Spoken Language Processing " by Xuedong Huang etc., "Computer Speech", by Manfr... |
AlaaElhilo/Wikipedia_ComputerScience | A good and accessible introduction to speech recognition technology and its history is provided by the general audience book "The Voice in the Machine. Building Computers That Understand Speech" by Roberto Pieraccini . |
AlaaElhilo/Wikipedia_ComputerScience | The most recent book on speech recognition is Automatic Speech Recognition: A Deep Learning Approach written by Microsoft researchers D. Yu and L. Deng and published near the end of 2014, with highly mathematically oriented technical detail on how deep learning methods are derived and implemented in modern speech reco... |
AlaaElhilo/Wikipedia_ComputerScience | In terms of freely available resources, Carnegie Mellon University's Sphinx toolkit is one place to start to both learn about speech recognition and to start experimenting. Another resource is the HTK book . For more recent and state-of-the-art techniques, Kaldi toolkit can be used. In 2017 Mozilla launched the open s... |
AlaaElhilo/Wikipedia_ComputerScience | Google Gboard supports speech recognition on all Android applications. It can be activated through the microphone icon. |
AlaaElhilo/Wikipedia_ComputerScience | The commercial cloud based speech recognition APIs are broadly available. |
AlaaElhilo/Wikipedia_ComputerScience | For more software resources, see List of speech recognition software. |
AlaaElhilo/Wikipedia_ComputerScience | Pattern recognition systems are commonly trained from labeled "training" data. When no labeled data are available, other algorithms can be used to discover previously unknown patterns. KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use. Pattern recognition focuses mo... |
AlaaElhilo/Wikipedia_ComputerScience | In machine learning, pattern recognition is the assignment of a label to a given input value. In statistics, discriminant analysis was introduced for this same purpose in 1936. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes . Pattern recog... |
AlaaElhilo/Wikipedia_ComputerScience | Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform "most likely" matching of the inputs, taking into account their statistical variation. This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns. ... |
AlaaElhilo/Wikipedia_ComputerScience | A modern definition of pattern recognition is: |
AlaaElhilo/Wikipedia_ComputerScience | Pattern recognition is generally categorized according to the type of learning procedure used to generate the output value. Supervised learning assumes that a set of training data has been provided, consisting of a set of instances that have been properly labeled by hand with the correct output. A learning procedure t... |
AlaaElhilo/Wikipedia_ComputerScience | Sometimes different terms are used to describe the corresponding supervised and unsupervised learning procedures for the same type of output. The unsupervised equivalent of classification is normally known as clustering, based on the common perception of the task as involving no training data to speak of, and of groupi... |
AlaaElhilo/Wikipedia_ComputerScience | The piece of input data for which an output value is generated is formally termed an instance. The instance is formally described by a vector of features, which together constitute a description of all known characteristics of the instance. These feature vectors can be seen as defining points in an appropriate multidim... |
AlaaElhilo/Wikipedia_ComputerScience | Many common pattern recognition algorithms are probabilistic in nature, in that they use statistical inference to find the best label for a given instance. Unlike other algorithms, which simply output a "best" label, often probabilistic algorithms also output a probability of the instance being described by the given l... |
AlaaElhilo/Wikipedia_ComputerScience | Feature selection algorithms attempt to directly prune out redundant or irrelevant features. A general introduction to feature selection which summarizes approaches and challenges, has been given. The complexity of feature-selection is, because of its non-monotonous character, an optimization problem where given a tota... |
AlaaElhilo/Wikipedia_ComputerScience | Techniques to transform the raw feature vectors are sometimes used prior to application of the pattern-matching algorithm. Feature extraction algorithms attempt to reduce a large-dimensionality feature vector into a smaller-dimensionality vector that is easier to work with and encodes less redundancy, using mathematic... |
AlaaElhilo/Wikipedia_ComputerScience | The problem of pattern recognition can be stated as follows: Given an unknown function
g
:
X
→
Y
{\displaystyle g:{\mathcal {X}}\rightarrow {\mathcal {Y}}}
... |
AlaaElhilo/Wikipedia_ComputerScience | For a probabilistic pattern recognizer, the problem is instead to estimate the probability of each possible output label given a particular input instance, i.e., to estimate a function of the form |
AlaaElhilo/Wikipedia_ComputerScience | where the feature vector input is
x
{\displaystyle {\boldsymbol {x}}}
, and the function f is typically parameterized by some parameters
θ
{\displaystyle {\boldsymbol {\theta }}}
. In a discrimi... |
AlaaElhilo/Wikipedia_ComputerScience | When the labels are continuously distributed , the denominator involves integration rather than summation: |
AlaaElhilo/Wikipedia_ComputerScience | The value of
θ
{\displaystyle {\boldsymbol {\theta }}}
is typically learned using maximum a posteriori estimation. This finds the best value that simultaneously meets two conflicting objects: To perform as well as possible on the training data and to fin... |
AlaaElhilo/Wikipedia_ComputerScience | where
θ
∗
{\displaystyle {\boldsymbol {\theta }}^{*}}
is the value used for
θ
{\displaystyle {\boldsymbol {\theta }}}
in the subsequen... |
AlaaElhilo/Wikipedia_ComputerScience | In the Bayesian approach to this problem, instead of choosing a single parameter vector
θ
∗
{\displaystyle {\boldsymbol {\theta }}^{*}}
, the probability of a given label for a new instance
... |
AlaaElhilo/Wikipedia_ComputerScience | The first pattern classifier – the linear discriminant presented by Fisher – was developed in the frequentist tradition. The frequentist approach entails that the model parameters are considered unknown, but objective. The parameters are then computed from the collected data. For the linear discriminant, these paramet... |
AlaaElhilo/Wikipedia_ComputerScience | Bayesian statistics has its origin in Greek philosophy where a distinction was already made between the 'a priori' and the 'a posteriori' knowledge. Later Kant defined his distinction between what is a priori known – before observation – and the empirical knowledge gained from observations. In a Bayesian pattern classi... |
AlaaElhilo/Wikipedia_ComputerScience | Probabilistic pattern classifiers can be used according to a frequentist or a Bayesian approach. |
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