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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
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
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
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
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 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
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)
Concept in information theory
information theory, machine learning, and statistical modeling. It is defined as P P ( p ) = ∏ x p ( x ) − p ( x ) = b − ∑ x p ( x ) log b p ( x ) , {\displaystyle
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
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
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
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
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
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
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)
Set of methods for supervised statistical learning
In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms
Support_vector_machine
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
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
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
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
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
Function in statistics
{\text{for}}\quad p\in (0,1).} Because of this, the logit is also called the log-odds since it is equal to the logarithm of the odds p 1 − p {\displaystyle
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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)
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
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
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
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
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
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
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
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
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
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
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
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)
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
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
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
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
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
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
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
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
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
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
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)
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
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
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
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
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
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
Type of computational algorithm
In computational complexity theory, a log-space reduction is a reduction computable by a deterministic Turing machine using logarithmic space. Conceptually
Log-space_reduction
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
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
Boosting algorithm
In machine learning and computational learning theory, LogitBoost is a boosting algorithm formulated by Jerome Friedman, Trevor Hastie, and Robert Tibshirani
LogitBoost
Exponentially decreasing bounds on tail distributions of random variables
log ( 1 − p 1 − q ) e log q p ) = − q log 1 − p 1 − q − q log q p + log ( 1 − p + p ( 1 − p 1 − q ) q p ) = − q log 1 − p 1 − q − q log
Chernoff_bound
Cybersecurity incident
In late April 2026, Canvas LMS, a learning management system operated by private company Instructure, was affected by a data breach and outage. Instructure
2026_Canvas_data_breach
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
Inequality of sum of product of number and logarithm of ratios
log sum inequality states that ∑ i = 1 n a i log a i b i ≥ a log a b , {\displaystyle \sum _{i=1}^{n}a_{i}\log {\frac {a_{i}}{b_{i}}}\geq a\log {\frac
Log_sum_inequality
Measure of complexity of real-valued functions
In computational learning theory (machine learning and theory of computation), Rademacher complexity, named after Hans Rademacher, measures richness of
Rademacher_complexity
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
airline pilot known for documenting the life of a pilot and promoting learning to fly. Drew Gooden United States Drew Gooden Known for his comedic commentary
List_of_YouTubers
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
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
Structured book club for young people
can take on many forms, ranging from writing, art, video/audiotapes, learning logs, student journals, personal responses etc. (Daniels, 1994). Extension
Literature_circle
Probabilistic model
probability theory, statistics—particularly Bayesian statistics—and machine learning. Generally, probabilistic graphical models use a graph-based representation
Graphical_model
Time to make a decision as a result of the possible choices
choose among the choices is approximately: T = b ⋅ log 2 ( n + 1 ) {\displaystyle T=b\cdot \log _{2}(n+1)} where b is a constant that can be determined
Hick's_law
Interactive geometry, algebra and calculus application
interactive geometry, algebra, statistics and calculus application, intended for learning and teaching mathematics and science from primary school to university
GeoGebra
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
Academic journal
Sciences Interdisciplinary Public Service Sustainability Teaching and Learning Innovation Global Management Locations Arizona Tempe Downtown Phoenix Polytechnic
Jurimetrics_(journal)
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
LEARNING LOG
LEARNING LOG
Boy/Male
Tamil
Vidaysagar | விதாயà¯à®¸à®¾à®•à®°
Learning ocean
Vidaysagar | விதாயà¯à®¸à®¾à®•à®°
Girl/Female
Tamil
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.
Surname or Lastname
English
English : variant of Leeming.
Girl/Female
Tamil
Vidhya | விதà¯à®¯à®¾,விதà¯à®¯à®¾Â
Knowledge, Learning
Vidhya | விதà¯à®¯à®¾,விதà¯à®¯à®¾Â
Surname or Lastname
English
English : unexplained. Probably a respelling of Irish Hearon.Possibly also an altered form of German Haering (see Hering).
Surname or Lastname
English
English : unexplained.
Girl/Female
Hindu
Learning
Surname or Lastname
English
English : variant spelling of Lanning.
Surname or Lastname
English
English : variant spelling of Waring.
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
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 : 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
Biblical
ploughing plough or till
Girl/Female
Biblical
Learning.
Girl/Female
Gujarati, Hindu, Indian
Learning
Biblical
learning
LEARNING LOG
LEARNING LOG
Boy/Male
Indian
The guide, Director, Leader
Boy/Male
Spanish American
Hispanic version of James: supplanter; he that replaces. Famous Bearer: famed Mexican artist...
Boy/Male
Hindu, Indian, Tamil
Shiva
Girl/Female
Biblical
Measure of a gift, preparation of a garment.
Boy/Male
Arthurian Legend Greek Latin
Uncle of Tristan.
Girl/Female
Indian
Goddess Durga
Boy/Male
Hindu, Indian
Surname
Girl/Female
Indian
A desire for something, Purpose, Bright, Lord Hanuman
Female
Italian
Feminine form of Italian Ilario, ILARIA means "joyful; happy."Â
Surname or Lastname
English
English : variant of Boniface.
LEARNING LOG
LEARNING LOG
LEARNING LOG
LEARNING LOG
LEARNING LOG
n.
The acquisition of knowledge or skill; as, the learning of languages; the learning of telegraphy.
n.
The act of gathering after reapers; that which is collected by gleaning.
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.
A line for hauling the reef cringle to the yard; -- also called reef earing.
n.
That which is signified, whether by act lanquage; signification; sence; import; as, the meaning of a hint.
n.
That which is meant or intended; intent; purpose; aim; object; as, a mischievous meaning was apparent.
a.
Giving previous notice; cautioning; admonishing; as, a warning voice.
n.
Purport; meaning; intended significance; aspect.
a.
Guiding; directing; controlling; foremost; as, a leading motive; a leading man; a leading example.
n.
A pilgrim bearing or wearing a cross.
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.
Attention to what is delivered; opportunity to be heard; audience; as, I could not obtain a hearing.
pl.
of Earning
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.
n.
The gross amount of the balances adjusted in the clearing house.
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.
n.
The act, or state, of inclining; inclination; tendency; as, a leaning towards Calvinism.
n.
Improperly, the unsupported span; as, the beam has twenty feet of bearing between its supports.
n.
The act, power, or time of producing or giving birth; as, a tree in full bearing; a tree past bearing.
a.
Pertaining to, or designed for, wear; as, wearing apparel.