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Supervised Learning
Published in Peter Wlodarczak, Machine Learning and its Applications, 2019
Training a machine learning algorithm is an iterative process and typically goes through many iterations until satisfactory results are obtained. Also, often several machine learning algorithms are trained and evaluated. The best performing model is then used to make predictions on new, unseen data. Usually, it is good practice to start with a simple model, such as a Bayesian model or the k-nearest neighbor learner, that can then be used as benchmark. If the results are not satisfactory, more sophisticated learning schemes can be trained and evaluated. If two learners perform equally well, the simpler one should be chosen in accordance with the Occam’s razor principle. The Occam’s razor principle states that if two problem solving solutions exist, the simpler one should be chosen. The ultimate goal is to have a model that isolates the signal and ignores the noise. How closely a models predicted values match the observed values is referred to as goodness of fit.
P
Published in Carl W. Hall, Laws and Models, 2018
Keywords: completion, time, work PARKINSON, Cyril Northcote, 1909-1993, English historian Source: Bothamley, J. 1993. PARSIMONY, LAW OF; ALSO KNOWN AS OCCAM RAZOR; MORGAN CANON If there are competing explanations for a scientific phenomenon, the simplest should be chosen. Animal behavior should be described in the simplest possible terms and should not include the attribution of human mental activities to animals, such as anecdotes, and projection of introspections. Keywords: animal, behavior, mental, simplest Sources: Good, C. V. 1978; Landau, S. I. 1986; Statt, D. 1982. See also OCCAM (OCKHAM) RAZOR; MORGAN CANON PARTIAL PRESSURES, LAW OF--SEE AMAGAT; DALTON PARTICIPATION, LAW OF In psychiatry this is the statement, thought, or belief that a person may have the sense that he is two different individuals at the same time, by psychotic patients, particularly in schizophrenia. Keywords: identification, personal, psychiatry, schizophrenia Source: Conger, G. P. 1924. PARTICLE NUMBER, Pn OR NPn A dimensionless group used to represent dust deposit in ducts relating terminal velocity and gravitational acceleration: Pn = UV/gL where U V g L = = = = terminal, free fall velocity of particle fluid velocity gravitational acceleration characteristic length
Architecture and Design
Published in Nikhilesh Krishnamurthy, Amitabh Saran, Building Software, 2007
Nikhilesh Krishnamurthy, Amitabh Saran
Occam’s razor is also called the Principle of Parsimony. These days, it is usually interpreted to mean something like “the simpler the explanation, the better” or “do not multiply hypotheses unnecessarily.” Occam’s razor has become a basic perspective for those who follow science and analytics; it is a guide to choosing the hypothesis that contains the least number of unproven assumptions. It must be noted that Occam’s razor never claims to choose the best theory, but only proposes simplicity as the deciding factor in choosing between two otherwise equal theories. It is possible that, given more information, the more complex theory might turn out to be correct.
Modelling the relationship between load and repetitions to failure in resistance training: A Bayesian analysis
Published in European Journal of Sport Science, 2023
Benedikt Mitter, Lei Zhang, Pascal Bauer, Arnold Baca, Harald Tschan
Models were compared in terms of model fit and model predictive accuracy. The model fit was analyzed by calculating a Bayesian R2 distribution (Gelman, Goodrich, Gabry, & Vehtari, 2019) and interpreted according to the Maximum a Posteriori estimate (MAP) and the 90% Highest Density Interval (HDI) (Makowski, Ben-Shachar, & Lüdecke, 2019). Differences between R2 posterior distributions were analyzed and interpreted according to their probability density overlap (∩R2) and deemed “substantial” for ∩R2 ≤ 5%. Model predictive accuracy was evaluated by calculating the expected log predictive density and converting it into a measure of deviance labelled LOOIC (Vehtari, Gelman, & Gabry, 2017), whereas smaller values of LOOIC indicate higher predictive validity. Differences in LOOIC between models (ΔLOOIC) were complemented with an estimated standard error of difference (SE) (Vehtari et al., 2017) and considered to be substantial if they exceeded 4x the SE. In cases of model comparisons not indicating substantial differences in model fit or predictive accuracy, models were further evaluated according to their simplicity. Under respective circumstances, the logical principle of Occam’s razor advocates that models with fewer parameters should be considered as more efficient. Posterior analysis was completed using R version 4.0.5 and the R packages bayestestR and loo.
Tensor decomposition to compress convolutional layers in deep learning
Published in IISE Transactions, 2022
Yinan Wang, Weihong “Grace” Guo, Xiaowei Yue
We understand that over-parameterization could be one of the main reasons that Deep Neural Networks have outstanding performance (Neyshabur et al., 2017). However, in engineering practice, according to the Occam’s Razor principle, when presented with competing models about the same prediction, one should select the solution with the fewest parameters. Based on the experiment results in Tables 3 and 4, the original design of CNN has a large redundancy in parameters. Our proposed method can provide an alternative way to control the model complexity. It can reduce the parameter redundancy without decaying its performance. In addition, it has the flexibility of adjusting the model complexity by selecting a suitable compression ratio. A model with fewer parameters usually provides us with better generalization and a better insight into the relationship between model parameters and extracted features, which will be discussed in the following section.
On the model building for transmission line cables: a Bayesian approach
Published in Inverse Problems in Science and Engineering, 2018
W. P. Hernández, D. A. Castello, C. F. T. Matt
Regarding model accuracy, one should describe and evaluate the predictive model capabilities in order to gain some confidence on its use. The accuracy of model predictions may be assessed in a quantitative way by means of model validation strategies [13–15]. As for model complexity, its definition is not unique. Nevertheless, there seems to be a consensus concerning complexity when the user has to select a model. This refers to the Principle of Model Parsimony or Occam’s razor which may be stated as follows: if a collection of models are compatible with a set of measured data, Occam’s razor advises us to choose the simplest one [16]. Although it seems quite abstract at first sight, the Occam’s razor is naturally connected with Bayesian inference and Bayesian model class selection, as can be seen in the work by MacKay [16] and also discussed afterwards in the current manuscript.