Studiebog
    Calculus of Thought

    Calculus of Thought

    - Neuromorphic Logistic Regression in Cognitive Machines

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    • Format
    • Bog, hardback
    • Engelsk

    Beskrivelse

    Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines is a must-read for all scientists about a very simple computation method designed to simulate big-data neural processing. This book is inspired by the Calculus Ratiocinator idea of Gottfried Leibniz, which is that machine computation should be developed to simulate human cognitive processes, thus avoiding problematic subjective bias in analytic solutions to practical and scientific problems. The reduced error logistic regression (RELR) method is proposed as such a "Calculus of Thought." This book reviews how RELR's completely automated processing may parallel important aspects of explicit and implicit learning in neural processes. It emphasizes the fact that RELR is really just a simple adjustment to already widely used logistic regression, along with RELR's new applications that go well beyond standard logistic regression in prediction and explanation. Readers will learn how RELR solves some of the most basic problems in today’s big and small data related to high dimensionality, multi-colinearity, and cognitive bias in capricious outcomes commonly involving human behavior.

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    Detaljer
    • SprogEngelsk
    • Sidetal272
    • Udgivelsesdato17-12-2013
    • ISBN139780124104075
    • Forlag Academic Press Inc
    • FormatHardback
    Størrelse og vægt
  • Vægt510 g
    • coffee cup img
    10 cm
    book img
    15,1 cm
    22,9 cm

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    Data mining Information theory Explanation Cognitive neuroscience Machine learning Resonance Positron-Emission Tomography Principal components analysis Logistic distribution Statistical mechanics Survival Analysis Kolmogorov complexity Magnetic resonance imaging Alpha rhythm Cognitive science Natural selection Attention Hippocampus Power series Neuroscience Case-control method Bias Causality Big data gestalt Alzheimer's disease Cognitive Aging Axon Observational Data Synapse Restricted Boltzmann Machine Bayes' Rule Naive bayes Longitudinal data Mixed logit Chi Square Maximum entropy Delayed recall Proportional hazards Dendrite Electroencephalography EEG Polynomial Random effects Linear Decision maker Prediction Logistic regression Likelihood ratio test Information gain Panel data Interactive learning Minimum Description Length Hypothesis testing Wald test Propensity score Normalization Discrete choice Multilayer perceptron Quadratic Maximum Likelihood Hierarchical Bayes Bayesian networks Genetic algorithm Back-propagation Neuron Magnetoencephalography MEG 'predictive analytics' Imputation Mutual Information Shannon Entropy Causal reasoning Unsupervised learning Supervised learning Fixed effects Taylor series Quasi-Experiment Ensemble learning Cross-Sectional Data Decision bias Binary variables Calculus Ratiocinator Analytic science Binary spikes Binding by synchrony Boltzmann entropy Cell body Beta Waves Boltzmann Machine Causal analytics Cluster-specific effects Bayesian probabilities Conditional logistic regression Dummy coded missing status variables Entorhinal Cortex Errors-in-variables regression Crystallized Intelligence cubic EEG left temporal slowing Fluid intelligence Dummy code Explicit RELR correlated observations Graded potentials Hebbian learning Generalized estimating equations (GEE) High-dimensional learning High Dimension Data Delta waves Hopfield Network Integrate-and-fire Implicit learning and memory Kullback-Leibler divergence (KL learning) Law of Pragnanz Intercept Correction Extreme value error Even polynomial features Local Field Potentials Explicit learning and memory Extreme value type 1 distribution K-means cluster analysis Medial Temporal Lobe Frequentist probabilities Neural loss Logit error Odd polynomial features Offset regression Gamma Waves Neuromorphic Hierarchical integration model of perception Nonlinear effect Neural impulse Hidden Units Quartic Immediate recall Ratio variables Implicit RELR Prior weight RELR sequential online learning Gumbel Distribution Randomized controlled experiment Interval variables Simple model of neural dynamics Small sample size learning RELR's outcome score matching Student's t Spearman Correlation Simpson's Paradox Theory of neuronal group selection Theta Waves Stable information theory Self-organized map Interaction effect Transitional Model Topsøe distance Jaynes principle Standardized Regression Coefficients Most likely inference Low Birth Weight Multicollinearity Update weight Synaptic sprouting Matched sample Matching experiment Welch's t Outcome score matching Posterior weight Neural Darwinism Probit regression Pseudo-observations Ranked variables RELR's causal machine learning Pearson correlation seasonal effects Negative error Ordinal Variables Reduced error logistic regression (RELR) Positive error Predementia Alzheimer's disease RELR error model assumptions Standardized variables Synaptic pruning Wechsler Logical Memory test

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