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Personalized pupil record
Learning Logs are a personalized learning resource for children. In the learning logs, the children record their responses to learning challenges set by
Learning_log
Machine learning technique
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Machine learning paradigm
In machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based
Supervised_learning
Relationship between proficiency and experience
measuring the strength of learning. It is usually expressed as n = log ( ϕ ) / log ( 2 ) {\displaystyle n=\log(\phi )/\log(2)} , where ϕ {\displaystyle
Learning_curve
Machine learning algorithm
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or
Decision_tree_learning
Smooth approximation to the maximum function
by machine learning algorithms. It is defined as the logarithm of the sum of the exponentials of the arguments: L S E ( x 1 , … , x n ) = log ( exp
LogSumExp
Field of machine learning
Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. While supervised learning and
Reinforcement_learning
Education technology specification by IMS Global Learning Consortium
requiring a learner to log in separately on the external systems. The LTI will also share learner information and the learning context shared by the LMS
Learning Tools Interoperability
Learning_Tools_Interoperability
Probability distribution
In probability theory, a log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally
Log-normal_distribution
Information-theoretic measure
defined as follows: H ( p , q ) = − E p [ log q ] , {\displaystyle H(p,q)=-\operatorname {E} _{p}[\log q],} where E p [ ⋅ ] {\displaystyle \operatorname
Cross-entropy
Mathematical function, inverse of an exponential function
formula: log b x = log 10 x log 10 b = log e x log e b . {\displaystyle \log _{b}x={\frac {\log _{10}x}{\log _{10}b}}={\frac {\log _{e}x}{\log _{e}b}}
Logarithm
Average uncertainty in variable's states
is H ( X ) := − ∑ x ∈ X p ( x ) log p ( x ) , {\displaystyle \mathrm {H} (X):=-\sum _{x\in {\mathcal {X}}}p(x)\log p(x),} where Σ {\displaystyle \Sigma
Entropy_(information_theory)
Mathematical model
A log-linear model is a mathematical model that takes the form of a function whose logarithm equals a linear combination of the parameters of the model
Log-linear_model
Machine learning method to transfer knowledge from a large model to a smaller one
In machine learning, knowledge distillation or model distillation is the process of transferring knowledge from a large model to a smaller one. While large
Knowledge_distillation
Concept in information theory
written as P P ( p ) = b − ∑ x p ( x ) log b p ( x ) , {\displaystyle \mathrm {PP} (p)=b^{-\sum _{x}p(x)\log _{b}p(x)},} where the value of b does not
Perplexity
Model of algorithmic learning
In computational learning theory, Occam learning is a model of algorithmic learning where the objective of the learner is to output a succinct representation
Occam_learning
Type of machine learning model
("Chinchilla scaling") for LLM autoregressively trained for one epoch, with a log-log learning rate schedule, states that: { C = C 0 N D L = A N α + B D β + L 0 {\displaystyle
Large_language_model
Set of methods for supervised statistical learning
In machine learning, a support vector machine (SVM) or support vector network is a supervised max-margin model with associated learning algorithms that
Support_vector_machine
y} . In the learning augmented algorithm, probing the positions i + 1 , i + 2 , i + 4 , … {\displaystyle i+1,i+2,i+4,\ldots } takes log 2 ( η ) {\displaystyle
Learning_augmented_algorithm
Concept in machine learning
In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price
Loss functions for classification
Loss_functions_for_classification
Real-time observability platform
Netdata provides over 800 integrations for metrics collection, log management, machine learning-based anomaly detection, and AI-assisted troubleshooting. The
Netdata
Algorithm for modelling sequential data
In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which text is
Transformer_(deep_learning)
Generalist medical doctor working in primary care
discussions, critique of videoed consultations and reflective entries into a "learning log". In addition, many hold qualifications such as the DCH (Diploma in Child
General_practitioner
Decentralized machine learning
Federated learning (also known as collaborative learning) is a machine learning technique in a setting where multiple entities (often called clients)
Federated_learning
Function in statistics
log-odds function is the quantile function associated with the standard logistic distribution. It has many uses in data analysis and machine learning
Logit
Tree-based ensemble machine learning methods
Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude
Random_forest
Quantum algorithm for integer factorization
{\displaystyle O\!\left((\log N)^{2}(\log \log N)(\log \log \log N)\right)} using fast multiplication, or even O ( ( log N ) 2 ( log log N ) ) {\displaystyle
Shor's_algorithm
Mathematical problem in cryptography
In cryptography, learning with errors (LWE) is a mathematical problem that is widely used to create secure encryption algorithms. It is based on the idea
Learning_with_errors
Quantity in information theory
thought of as an alternative way of expressing probability, much like odds or log-odds, but which has particular mathematical advantages in the setting of
Information_content
Relationship between experience producing a good and the efficiency of that production
production (learning rate). To see this, note the following: C 2 x = C 1 ( 2 x ) log 2 ( b ) = C 1 x log 2 ( b ) ⋅ 2 log 2 ( b ) = C x ⋅ 2 log 2 ( b
Experience_curve_effect
Measure of ranking quality
e l i log 2 ( i + 1 ) = r e l 1 + ∑ i = 2 p r e l i log 2 ( i + 1 ) {\displaystyle \mathrm {DCG_{p}} =\sum _{i=1}^{p}{\frac {rel_{i}}{\log _{2}(i+1)}}=rel_{1}+\sum
Discounted_cumulative_gain
Iterative method for finding maximum likelihood estimates in statistical models
expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a
Expectation–maximization algorithm
Expectation–maximization_algorithm
Technique used in stochastic gradient variational inference
"reparameterization gradient estimator") is a technique used in statistical machine learning, particularly in variational inference, variational autoencoders, and stochastic
Reparameterization_trick
Book by A. S. Neill
A.S. Neill's A Dominie's Log is a diary of his first year as headteacher at Gretna Green Village School, during 1914–15. It is an autobiographical novel
A_Dominie's_Log
Machine learning paradigm
Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals
Self-supervised_learning
meet the harm threshold. It can also be used as part of a GP trainee's learning log. The value of using SEA was highlighted in the publication of the GP
Significant_event_audit
In computer log management and intelligence, log analysis (or system and network log analysis) is an art and science seeking to make sense of computer-generated
Log_analysis
Overview of and topical guide to machine learning
is provided as an overview of, and topical guide to, machine learning: Machine learning (ML) is a subfield of artificial intelligence within computer
Outline_of_machine_learning
Historic building in Pennsylvania, US
The Log Cabin at the University of Pittsburgh, located near Forbes Avenue, in Pittsburgh, Pennsylvania adjacent to the school's Cathedral of Learning, serves
Log Cabin (University of Pittsburgh)
Log_Cabin_(University_of_Pittsburgh)
Concept in machine learning
Double descent in statistics and machine learning is the phenomenon where a model's error rate on the test set initially decreases with the number of parameters
Double_descent
Mathematical statistics distance measure
Q ) = ∑ x ∈ X P ( x ) log P ( x ) Q ( x ) . {\displaystyle D_{\text{KL}}(P\parallel Q)=\sum _{x\in {\mathcal {X}}}P(x)\,\log {\frac {P(x)}{Q(x)}}{\text{
Kullback–Leibler_divergence
Use of machine learning to rank items
Learning to rank (LTR) or machine-learned ranking (MLR) is the application of machine learning, often supervised, semi-supervised or reinforcement learning
Learning_to_rank
Particular case of the generalized extreme value distribution
(also known as the Fisher–Tippett distribution). It is also known as the log-Weibull distribution and the double exponential distribution (a term that
Gumbel_distribution
Paradigm in machine learning
Weak supervision (also known as semi-supervised learning) is a paradigm in machine learning, the relevance and notability of which increased with the
Weak_supervision
Probabilistic classification algorithm
when expressed in log-space: log p ( C k ∣ x ) ∝ log ( p ( C k ) ∏ i = 1 n p k i x i ) = log p ( C k ) + ∑ i = 1 n x i ⋅ log p k i = b + w k ⊤
Naive_Bayes_classifier
Educational hand-held game console
game and allows games to log user data, such as topics learned or user-created art. Logged activity is sent to LeapFrog's "Learning Path" system, which tracks
Leapster
The distributional learning theory or learning of probability distribution is a framework in computational learning theory. It has been proposed from Michael
Distribution_learning_theory
Market town in Norfolk, England
March 2018). "Exercise 3.5: Local History". Bob Coe's OCA Landscape Learning Log. Retrieved 17 October 2019.{{cite web}}: CS1 maint: numeric names: authors
Wymondham
Measure of error in statistics
( log 0.9 + log 0.4 + log 0.7 + log 0.8 + log 0.4 + log 0.3 ) = 3.72 {\displaystyle -(\log 0.9+\log 0.4+\log 0.7+\log 0.8+\log 0.4+\log 0
Negative log predictive density
Negative_log_predictive_density
Statistical model for a binary dependent variable
a logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent variables
Logistic_regression
Probability distribution
exponential family of distributions. Writing θ = log ( λ / ( 1 − λ ) ) {\displaystyle \theta =\log \left(\lambda /(1-\lambda )\right)} for the natural
Continuous Bernoulli distribution
Continuous_Bernoulli_distribution
Measure of dependence between two variables
1 2 log ( 2 π e σ i 2 ) = 1 2 + 1 2 log ( 2 π ) + log ( σ i ) , i ∈ { 1 , 2 } H ( X 1 , X 2 ) = 1 2 log [ ( 2 π e ) 2 | Σ | ] = 1 + log ( 2
Mutual_information
Technique for the generative modeling of a continuous probability distribution
In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable
Diffusion_model
Method of machine learning
improved further to a O ( log T ) {\displaystyle O(\log T)} for strongly convex and exp-concave loss functions. Continual learning means constantly improving
Online_machine_learning
Statistical model used in machine learning
{\displaystyle \log p_{K}(z_{K})=\log p_{0}(z_{0})-\sum _{i=1}^{K}\log \left|\det {\frac {df_{i}(z_{i-1})}{dz_{i-1}}}\right|} Learning probability distributions
Flow-based_generative_model
Method of data analysis
RPCA to O ( max { m , n } r 2 log ( m ) log ( n ) log 1 ϵ ) {\displaystyle O\left(\max\{m,n\}r^{2}\log(m)\log(n)\log {\frac {1}{\epsilon }}\right)}
Robust principal component analysis
Robust_principal_component_analysis
Transforming data by taking the logarithm
In statistics, the log transformation is the application of the logarithmic function to each point in a data set—that is, each data point zi is replaced
Log transformation (statistics)
Log_transformation_(statistics)
Structuring text as input to generative artificial intelligence
in-context learning is temporary. Training models to perform in-context learning can be viewed as a form of meta-learning, or "learning to learn". Research
Prompt_engineering
Basic concepts of algebra
{\displaystyle 2^{x-1}=3} whence x − 1 = log 2 3 {\displaystyle x-1=\log _{2}3} or x = log 2 3 + 1. {\displaystyle x=\log _{2}3+1.} A logarithmic equation
Elementary_algebra
Smoothed ramp function
multivariable generalization of the logistic function. Both LogSumExp and softmax are used in machine learning. The convex conjugate (specifically, the Legendre
Softplus
Academic discipline; examines how goal-driven social entities add and create knowledge
Christina Fang. The Muth model (1986) was the first to represent the learning curve in a log-linear form and focused on cost effectiveness in organization processes
Organizational_learning
Generalized version of the Akaike information criterion
take log pointwise predictive density: lppd ( y , Θ ) = ∑ i log 1 S ∑ s p ( y i ∣ Θ s ) {\displaystyle {\text{lppd}}(y,\Theta )=\sum _{i}\log {\frac
Watanabe–Akaike information criterion
Watanabe–Akaike_information_criterion
Grouping a set of objects by similarity
retrieval, bioinformatics, data compression, computer graphics and machine learning. Cluster analysis refers to a family of algorithms and tasks rather than
Cluster_analysis
Interdisciplinary research area
Quantum machine learning (QML) is the study of quantum algorithms for machine learning. It often refers to quantum algorithms for machine learning tasks which
Quantum_machine_learning
Action of recording the keys struck on a keyboard
Keystroke logging, often referred to as keylogging or keyboard capturing, is the action of recording (logging) the keys pressed on a keyboard, typically
Keystroke_logging
Estimate of time taken for running an algorithm
multiplication, O ( n log n log log n ) {\displaystyle O(n\log n\log \log n)} In many cases, the O ( n log n ) {\displaystyle O(n\log n)} running time
Time_complexity
Concept in artificial intelligence
intelligence, apprenticeship learning (or learning from demonstration or imitation learning) is the process of learning by observing an expert. It can
Apprenticeship_learning
Notion in supervised machine learning
set) is given by: Pr ( test error ⩽ training error + 1 N [ D ( log ( 2 N D ) + 1 ) − log ( η 4 ) ] ) = 1 − η , {\displaystyle \Pr \left({\text{test
Vapnik–Chervonenkis_dimension
Building at the University of Pittsburgh
The Cathedral of Learning is a 42-story skyscraper that serves as the centerpiece of the University of Pittsburgh's (Pitt) main campus in the Oakland neighborhood
Cathedral_of_Learning
Optimization and sampling technique
{\displaystyle LR^{2}} being O ( log d ) {\displaystyle {\mathcal {O}}(\log d)} . Welling, Max; Teh, Yee Whye (2011). "Bayesian Learning via Stochastic Gradient
Stochastic gradient Langevin dynamics
Stochastic_gradient_Langevin_dynamics
Smooth approximation of one-hot arg max
computational complexity from O ( K ) {\displaystyle O(K)} to O ( log 2 K ) {\displaystyle O(\log _{2}K)} . In practice, results depend on choosing a good strategy
Softmax_function
Probabilistic logic programming language
samples of q {\displaystyle q} Learning from interpretations: learn the probabilities of ProbLog programs from data ProbLog can for example be used to calculate
ProbLog
Computer science data structure
O(n log n) storage and can be built in O(n log n) time. Segment trees support searching for all the intervals that contain a query point in time O(log n
Segment_tree
Approximation for factorials
equivalent form log 2 n ! = n log 2 n − n log 2 e + O ( log 2 n ) . {\displaystyle \log _{2}n!=n\log _{2}n-n\log _{2}e+O(\log _{2}n).} The error
Stirling's_approximation
Technique in natural language processing
as: g i = 1 + ∑ j p i j log p i j log n {\displaystyle g_{i}=1+\sum _{j}{\frac {p_{ij}\log p_{ij}}{\log n}}} a i j = g i log ( t f i j + 1 ) {\displaystyle
Latent_semantic_analysis
Mathematical model used for classification or regression
are a class of models frequently used for classification. In machine learning, it typically models the conditional distribution P(Y∣X), or it learns
Discriminative_model
Algorithm for supervised learning of binary classifiers
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that can decide whether
Perceptron
Technique used in statistics
Log-linear analysis is a technique used in statistics to examine the relationship between more than two categorical variables. The technique is used for
Log-linear_analysis
Regression analysis for modeling ordinal data
[yi = k].) The log-likelihood of the ordered logit model is analogous, using the logistic function instead of Φ. In machine learning, alternatives to
Ordinal_regression
Concept in machine learning
In statistics and machine learning, leakage (also known as data leakage or target leakage) refers to the use of information during model training that
Leakage_(machine_learning)
Real-time communication over the internet
synchronous conferencing are: Chat (text only): Multiple participants can be logged into the conference and can interactively share resources and ideas. There
Online_chat
Decline of memory retention in time
approximate his forgetting curve: b = 100 k ( log ( t ) ) c + k {\displaystyle b={\frac {100k}{(\log(t))^{c}+k}}} Here, b {\displaystyle b} represents
Forgetting_curve
Subscription based education program for children 2–8
Early Learning Academy, is a digital education program targeted towards children ages 2–8, created by the educational technology company Age of Learning, Inc
ABCmouse
Philosophy of teaching
are: audio and visual recordings samples of children's work photos learning logs display boards These approaches can help students develop pride in their
Emergent_curriculum
Nature preserve in Denton County, Texas
on March 23, 2022. Retrieved March 23, 2022. "1869 Log House | Lewisville Lake Environmental Learning Area". www.llela.org. Retrieved March 24, 2022. "Hiking
Lewisville Lake Environmental Learning Area
Lewisville_Lake_Environmental_Learning_Area
British painter (1803–1886)
design is attributed to Obadiah Short can be seen at Nicky Eastaugh's learning log for Textiles 1: Mixed Media for Textiles. Hoyte 2010, p. 39. "History
Obadiah_Short
Notion in statistics
the variance of the score: I ( θ ) = E [ ( ∂ ∂ θ log f ( X ; θ ) ) 2 | θ ] = ∫ R ( ∂ ∂ θ log f ( x ; θ ) ) 2 f ( x ; θ ) d x , {\displaystyle {\mathcal
Fisher_information
Class of variational quantum states
}(S^{(i)}),} where O ( S ( i ) ) = ∂ log F ( S ( i ) ; W ) ∂ W k {\displaystyle O(S^{(i)})={\frac {\partial \log F(S^{(i)};W)}{\partial W_{k}}}} and
Neural_network_quantum_states
American educational technology company
databases and students can log in with a badge or a password. Once logged in to clever, students can easily find available learning apps on their homepage
Clever_(company)
Scientific study of digital information
is 1/2 and the amount of information is expressed as − log 2 ( 1 / 2 ) {\displaystyle -\log _{2}(1/2)} = 1 bit of information. A key concept in information
Information_theory
Set of statistical processes for estimating the relationships among variables
(often called the outcome or response variable, or a label in machine learning parlance) and one or more independent variables (often called regressors
Regression_analysis
Algorithm from machine learning
by: α k ( log α Θ + 1 ) + n Θ {\displaystyle \alpha k(\log _{\alpha }\Theta +1)+{\frac {n}{\Theta }}} . Nick Littlestone (1988). "Learning Quickly When
Winnow_(algorithm)
Research field that lies at the intersection of machine learning and computer security
Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. Machine learning techniques
Adversarial_machine_learning
Type of artificial neural network
In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple
Deep_belief_network
Method of data analysis
space associated with a positive definite kernel. In multilinear subspace learning, PCA is generalized to multilinear PCA (MPCA) that extracts features directly
Principal_component_analysis
Indian educationist and philanthropist
Nitish's leadership, S P Jain is credited with pioneering a multi-city learning model and developing a proprietorial software for the delivery of online
Nitish_Jain
Concept in probability and statistics
_{\theta }\log(l(\theta ))} where log ( l ( θ ) ) = log ( P ( x 1 | θ ) ) + log ( P ( x 2 | θ ) ) + log ( P ( x 3 | θ ) ) + . . . + log ( P ( x
Independent and identically distributed random variables
Independent_and_identically_distributed_random_variables
Probabilistic model
probability theory, statistics—particularly Bayesian statistics—and machine learning. Generally, probabilistic graphical models use a graph-based representation
Graphical_model
Approach in generative models
Ensemble Learning (CEL) or Learning via Canonical Ensemble (LCE), is an application of canonical ensemble formulation from statistical physics for learning from
Energy-based_model
Statistical model
hyperparameters θ. As such the log marginal likelihood is: log p ( f ( x ′ ) ∣ θ , x ) = − 1 2 ( f T ( x ) K − 1 ( θ , x , x ′ ) f ( x ′ ) + log det ( K ( θ , x
Gaussian_process
LEARNING LOG
LEARNING LOG
Surname or Lastname
English
English : unexplained. Probably a respelling of Irish Hearon.Possibly also an altered form of German Haering (see Hering).
Girl/Female
Biblical
Learning.
Boy/Male
Tamil
Vidaysagar | விதாயà¯à®¸à®¾à®•à®°
Learning ocean
Vidaysagar | விதாயà¯à®¸à®¾à®•à®°
Surname or Lastname
English (Dorset and Somerset)
English (Dorset and Somerset) : unexplained.Dutch : patronymic from a short form of the personal name Julianus (see Julian).
Surname or Lastname
English
English : variant spelling of Lanning.
Girl/Female
Tamil
Learning
Surname or Lastname
English
English : patronymic from Dear 1.Americanized spelling of German Diering, a variant of Döring (see Doering).
Boy/Male
Hindu
Learning ocean
Girl/Female
Hindu
Learning
Girl/Female
Tamil
Vidhya | விதà¯à®¯à®¾,விதà¯à®¯à®¾Â
Knowledge, Learning
Vidhya | விதà¯à®¯à®¾,விதà¯à®¯à®¾Â
Girl/Female
Gujarati, Hindu, Indian
Learning
Surname or Lastname
English
English : unexplained.
Surname or Lastname
English
English : variant of Leeming.
Biblical
learning
Surname or Lastname
English
English : patronymic from a Germanic personal name beginning with the element gÄ“r, gÄr ‘spear’ (see Geary 2).Probably an Americanized spelling of German Gehring.
Girl/Female
Arabic, Muslim, Parsi
Learning; Wisdom
Girl/Female
Sikh
Knowledge, Learning
Surname or Lastname
English
English : habitational name from Feering, a village in Essex, named from the Old English personal name Fēra + -ingas ‘people of’, i.e. ‘(settlement of) Fēra’s people’.Americanized spelling of German Viering, a topographic name for someone from a swampy area, from a derivative of Germanic vir ‘bog’, ‘swamp’, or a variant of Fehring 2.
Biblical
ploughing plough or till
Surname or Lastname
English
English : variant spelling of Waring.
LEARNING LOG
LEARNING LOG
Girl/Female
Australian, French
Of the Nobility; Noble
Girl/Female
Hindu
Goddess Lakshmi
Boy/Male
Arabic, Muslim, Pashtun
Laughing
Boy/Male
American, Arabic, Australian, British, Chinese, Christian, Danish, Dutch, English, French, German, Greek, Irish, Jamaican, Latin, Muslim
Light Giving; Light; Bringer of Light; A Region of Southern Italy; Native of Lucania; Bright; Form of Lucus
Female
African
urgent business.
Female
Russian
(ТальÑ) Short form of Russian Natalya, TALYA means "birthday," or in Church Latin "Christmas day." Compare with other forms of Talya.
Girl/Female
American, Australian, British, English, French, Greek, Latin
Resurrection; Fruitful; Shall be Reborn; Form of Anastasia; Giving Fruit
Girl/Female
Indian
Glory of a king, Bride
Boy/Male
Arabic, Muslim
Name of Famous People; Including; Abu Al-husayn Muhammad; A Student of Al-ashari and Abu Umer Muhammad
Girl/Female
Tamil
LEARNING LOG
LEARNING LOG
LEARNING LOG
LEARNING LOG
LEARNING LOG
a.
Guiding; directing; controlling; foremost; as, a leading motive; a leading man; a leading example.
n.
That which is signified, whether by act lanquage; signification; sence; import; as, the meaning of a hint.
n.
Attention to what is delivered; opportunity to be heard; audience; as, I could not obtain a hearing.
n.
That which is meant or intended; intent; purpose; aim; object; as, a mischievous meaning was apparent.
a.
Pertaining to, or designed for, wear; as, wearing apparel.
n.
The act of gathering after reapers; that which is collected by gleaning.
n.
The act, power, or time of producing or giving birth; as, a tree in full bearing; a tree past bearing.
n.
Purport; meaning; intended significance; aspect.
a.
Giving previous notice; cautioning; admonishing; as, a warning voice.
n.
The acquisition of knowledge or skill; as, the learning of languages; the learning of telegraphy.
n.
The act, or state, of inclining; inclination; tendency; as, a leaning towards Calvinism.
n.
The act or power of perceiving sound; perception of sound; the faculty or sense by which sound is perceived; as, my hearing is good.
pl.
of Earning
n.
The knowledge or skill received by instruction or study; acquired knowledge or ideas in any branch of science or literature; erudition; literature; science; as, he is a man of great learning.
n.
The gross amount of the balances adjusted in the clearing house.
n.
That part of any member of a building which rests upon its supports; as, a lintel or beam may have four inches of bearing upon the wall.
n.
A pilgrim bearing or wearing a cross.
n.
Improperly, the unsupported span; as, the beam has twenty feet of bearing between its supports.
n.
A line for hauling the reef cringle to the yard; -- also called reef earing.
n.
The parts by which motion imparted to one portion of an engine or machine is transmitted to another, considered collectively; as, the valve gearing of locomotive engine; belt gearing; esp., a train of wheels for transmitting and varying motion in machinery.