Calculus of Thought: Neuromorphic Logistic Regression in by Daniel M. Rice PDF

By Daniel M. Rice

Calculus of proposal: Neuromorphic Logistic Regression in Cognitive Machines is a must-read for all scientists a few extremely simple computation strategy designed to simulate big-data neural processing. This booklet is galvanized by means of the Calculus Ratiocinator proposal of Gottfried Leibniz, that's that laptop computation will be built to simulate human cognitive methods, therefore warding off not easy subjective bias in analytic options to useful and medical problems.

The decreased mistakes logistic regression (RELR) procedure is proposed as this kind of "Calculus of Thought." This publication reports how RELR's thoroughly computerized processing could parallel vital features of particular and implicit studying in neural tactics. It emphasizes the truth that RELR is admittedly only a easy adjustment to already favourite logistic regression, in addition to RELR's new purposes that pass well past average logistic regression in prediction and rationalization. Readers will find out how RELR solves probably the most easy difficulties in today’s titanic and small information on the topic of excessive dimensionality, multi-colinearity, and cognitive bias in capricious results typically related to human behavior.

• presents a high-level creation and specified stories of the neural, statistical and computing device studying wisdom base as a starting place for a brand new period of smarter machines
• Argues that smarter computer studying to deal with either clarification and prediction with no cognitive bias should have a starting place in cognitive neuroscience and needs to include comparable specific and implicit studying ideas that take place within the brain
• bargains a brand new neuromorphic beginning for desktop studying dependent upon the diminished mistakes logistic regression (RELR) technique and gives easy examples of RELR computations in toy difficulties that may be accessed in spreadsheet workbooks via a better half site

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Extra info for Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines

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The slight modification to standard logistic regression that is RELR simply ensures that error in measurements and inferences are effectively taken into account as a part of the basic logistic regression formulation. In the terms of regression modeling theory, RELR is an error-in-variables type method56 that estimates the error in the independent variables in terms of well-known properties instead of assuming that such error is zero. Modifications to standard logistic regression in the newer penalized forms of logistic regression also attempt to correct these multicollinearity deficiencies, although these are not error-in-variables methods because unlike RELR they do not estimate the error in the independent variables.

There is a nontrivial amount of error in the logistic regression model’s probability estimates for these choices, as the regression model only provides a best or maximum likelihood fit. 2 Example of substantial error that is usually seen in logistic regression models. The gray curve reflects the probability of the responses, whereas the black curve reflects binary (0, 1) responses across 14 consecutive trials in simulated data. The error that is defined in discrete choice modeling is represented by the difference between these two curves.

7) has the same form as an ordinary linear regression equation although there is no error term. Like linear regression, the features in the logit are independent variables that combine in a linear additive manner and also can be nonlinear and interaction effect terms, so a logit model that arises from logistic regression is not limited just in terms of linear main effect features. Like ordinary linear regression, a problem with standard logistic regression is that overfitting error and related multicollinearity error occur as more features are added, and this can be especially problematic with nonlinear and interaction effects.

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Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines by Daniel M. Rice


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