Input: In particular, they are used during the calculation of likelihood of a tree (in Bayesian and maximum likelihood approaches to tree estimation) and they are used to estimate the evolutionary distance between sequences from the observed differences between the sequences.

Bayesian

maximum likelihood


Input: The Audio Engineering Society recommends 48 kHz sampling rate for most applications but gives recognition to 44.1 kHz for Compact Disc (CD) and other consumer uses, 32 kHz for transmission-related applications, and 96 kHz for higher bandwidth or relaxed anti-aliasing filter ing.

Audio Engineering Society

Compact Disc

CD

anti-aliasing filter


Input: Resources for affectivity of words and concepts have been made for WordNet {{cite journal

WordNet


Input: In red-green anaglyph, the audience was presented three reels of tests, which included rural scenes, test shots of Marie Doro, a segment of John B. Mason playing a number of passages from Jim the Penman (a film released by Famous Players-Lasky that year, but not in 3D), Oriental dancers, and a reel of footage of Niagara Falls.

red-green anaglyph

Marie Doro

John B. Mason

Jim the Penman

Famous Players-Lasky

Niagara Falls


Input: This is a particular way of implementing maximum likelihood estimation for this problem.

maximum likelihood estimation


Input: Crawler-friendly Web Servers, and it integrates the features of sitemaps and RSS feeds into a decentralized mechanism for computational biologists and bio-informaticians to openly broadcast and retrieve meta-data about biomedical resources.

Crawler-friendly Web Servers

RSS


Input: It is covered by American National Standards Institute / NISO standard Z39.50, and International Organization for Standardization standard 23950.

American National Standards Institute / NISO standard Z39.50

International Organization for Standardization standard 23950


Input: The encoder and decoder are trained to take a phrase and reproduce the one-hot distribution of a corresponding paraphrase by minimizing perplexity using simple stochastic gradient descent.

one-hot distribution

perplexity

stochastic gradient descent


Input: Other typical applications of pattern recognition techniques are automatic speech recognition, classification of text into several categories (e.g., spam / non-spam email messages), the handwriting recognition on postal envelopes, automatic recognition of images of human faces, or handwriting image extraction from medical forms.

pattern recognition

automatic speech recognition

classification of text into several categories

handwriting recognition on postal envelopes

automatic recognition of images of human faces

handwriting image extraction from medical forms


Input: Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.

Artificial neural networks

computer vision

speech recognition

machine translation

social network filtering

playing board and video games

medical diagnosis


Input: Examples include Salford Systems CART (which licensed the proprietary code of the original CART authors), IBM SPSS Modeler, RapidMiner, SAS Enterprise Miner, Matlab, R (an open-source software environment for statistical computing, which includes several CART implementations such as rpart, party and randomForest packages), Weka (a free and open-source data-mining suite, contains many decision tree algorithms), Orange, KNIME, Microsoft SQL Server programming language).

Salford Systems

IBM

CART

CART

SPSS Modeler

RapidMiner

SAS Enterprise Miner

Matlab

CART

Weka

Orange

KNIME

Microsoft SQL Server

R

statistical computing

rpart

party

randomForest

decision tree

data-mining


Input: Linear predictive coding (LPC) was first developed by Fumitada Itakura of Nagoya University and Shuzo Saito of Nippon Telegraph and Telephone (NTT) in 1966, and then further developed by Bishnu S. Atal and Manfred R. Schroeder at Bell Labs during the early-to-mid-1970s, becoming a basis for the first speech synthesizer DSP chips in the late 1970s.

Linear predictive coding

LPC

Fumitada Itakura

Shuzo Saito

Bishnu S. Atal

Manfred R. Schroeder

Nagoya University

Nippon Telegraph and Telephone

NTT

Bell Labs

speech synthesizer DSP chips


Input: An F-score is a combination of the precision and the recall, providing a single score.

F-score

precision

recall


Input: Image analysis tasks can be as simple as reading bar code d tags or as sophisticated as facial recognition system.

Image analysis

reading bar code d tags

facial recognition system


Input: The special case of linear support-vector machines can be solved more efficiently by the same kind of algorithms to optimize its close cousin, logistic regression; this class of algorithms includes Stochastic gradient descent (e.g., PEGASOS).

support-vector machines

logistic regression

Stochastic gradient descent

PEGASOS


Input: When Siri on an iOS device is asked Do you have a pet?, one the responses is I used to have an AIBO.

Siri

iOS

AIBO


Input: In information retrieval, the positive predictive value is called precision, and sensitivity is called recall.

information retrieval

positive predictive value

precision

sensitivity

recall


Input: In particular, his research focused on areas such as text mining (extraction, categorization, novelty detection) and in new theoretical frameworks such as a unified utility-based theory bridging information retrieval, Automatic summarization, free-text Question Answering and related tasks.

text mining

extraction

categorization

novelty detection

information retrieval

Automatic summarization

free-text Question Answering


Input: Delta robot s have base-mounted rotary actuator s that move a light, stiff, parallelogram arm.

Delta robot

rotary actuator

parallelogram arm


Input: The four outcomes can be formulated in a 2 × 2 contingency table or confusion matrix, as follows:

2 × 2 contingency table

confusion matrix


Input: The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining).

data mining

cluster analysis

anomaly detection

association rule mining

sequential pattern mining


Input: For a recommender system, sentiment analysis has been proven to be a valuable technique.

recommender system

sentiment analysis


Input: By chance, the Germans had chosen the operating frequency of the Wotan system very badly; it operated on 45 MHz, which just happened to be the frequency of the powerful-but-dormant BBC television transmitter at Alexandra Palace.

Germans

Wotan

BBC

Alexandra Palace


Input: The four outcomes can be formulated in a 2 × 2 contingency table or confusion matrix, as follows:

2 × 2 contingency table

confusion matrix


Input: In Semantic Web applications, and in relatively popular applications of RDF like RSS and FOAF (Friend a Friend), resources tend to be represented by URIs that intentionally denote, and can be used to access, actual data on the World Wide Web.

Semantic Web applications

RDF

URIs

RSS

FOAF

Friend a Friend

World Wide Web


Input: The Association for the Advancement of Artificial Intelligence has studied this topic in depth

Association for the Advancement of Artificial Intelligence


Input: Starting as a curiosity, the speech system of Apple Macintosh has evolved into a fully supported program PlainTalk, for people with vision problems.

speech system of Apple Macintosh

PlainTalk


Input: Other areas of usage for ontologies within NLP include information retrieval, information extraction and automatic summarization.

NLP

information retrieval

information extraction

automatic summarization


Input: The Institute has collaborated closely with the Janelia Farm Campus of Howard Hughes Medical Institute, the Allen Institute for Brain Science and the National Institutes of Health to develop better methods of reconstructing neuronal architectures.

Janelia Farm Campus of Howard Hughes Medical Institute

Allen Institute for Brain Science

National Institutes of Health


Input: Recently, Google announced that Google Translate translates roughly enough text to fill 1 million books in one day (2012).

Google

Google Translate


Input: Events are held worldwide, and are most popular in the United Kingdom, United States, Japan, Singapore, India, South Korea and becoming popular in subcontinent countries such as Sri Lanka.

United Kingdom

United States

Japan

Singapore

India

South Korea

Sri Lanka


Input: These packages are developed primarily in R, and sometimes in Java, C, C + +, and Fortran.

R

Java

C

C + +

Fortran


Input: As part of the 2006 European Conference on Computer Vision (ECCV), Dalal and Triggs teamed up with Cordelia Schmid to apply HOG detectors to the problem of human detection in films and videos.

2006 European Conference on Computer Vision

ECCV

Dalal

Triggs

Cordelia Schmid

HOG detectors

human detection in films and videos


Input: In addition to sensitivity and specificity, the performance of a binary classification test can be measured with positive predictive value (PPV), also known as precision, and negative predictive value (NPV).

sensitivity

specificity

positive predictive value

PPV

precision

negative predictive value

NPV

binary classification


Input: Such models may given partial credit for overlapping matches (such as using the Jaccard index criterion.

Jaccard index criterion


Input: Further, in the case of estimation based on a single sample, it demonstrates philosophical issues and possible misunderstandings in the use of maximum likelihood estimators and likelihood functions.

maximum likelihood estimators and likelihood functions