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First Order Logic
What Is First Order Logic First-order logic is a collection of formal systems that are utilized in the fields of mathematics, philosophy, linguistics, and computer science. Other names for first-order logic include predicate logic, quantificational logic, and first-order predicate calculus. In first-order logic, quantified variables take precedence over non-logical objects, and the use of sentences that contain variables is permitted. As a result, rather than making assertions like "Socrates is a man," one can make statements of the form "there exists x such that x is Socrates and x is a man," where "there exists" is a quantifier and "x" is a variable. This is in contrast to propositional lo...
Naive Bayes Classifier
What Is Naive Bayes Classifier In the field of statistics, naive Bayes classifiers are a family of straightforward "probabilistic classifiers" that are derived from the application of Bayes' theorem with strong (naive) assumptions of independence between the features. They are among the Bayesian network models that are the simplest, but when combined with kernel density estimation, they are capable of achieving great levels of accuracy. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Naive Bayes classifier Chapter 2: Likelihood function Chapter 3: Bayes' theorem Chapter 4: Bayesian inference Chapter 5: Multivariate normal distribution Chapter 6: Maxi...
Hidden Markov Model
What Is Hidden Markov Model A hidden Markov model, often known as an HMM, is a type of statistical Markov model. In an HMM, the system being represented is considered to be a Markov process, which we will refer to as it, with states that cannot be observed (thus the name "hidden"). In order to fulfill one of the requirements for the definition of HMM, there must be a measurable process whose results are "influenced" by those of another process in a certain way. Since it is not possible to directly see, the objective here is to learn about via observing. HMM contains the additional criterion that the result of an event that occurs at a certain time must be "influenced" solely by the outcome o...
Modal Logic
What Is Modal Logic Statements regarding necessity and possibility can be represented with the use of a type of logic known as modal logic. As a method for gaining a grasp of ideas like knowledge, obligation, and causality, it is an essential component of philosophy and other subjects that are closely related to it. For instance, the formula can be used to describe the statement that is known in the epistemic modal logic. Using the same formula, one can express that which is a moral responsibility within the framework of deontic modal logic. The conclusions that can be drawn from modal assertions are taken into consideration by modal logic. For instance, the majority of epistemic logics cons...
Radial Basis Networks
What Is Radial Basis Networks A radial basis function network is a type of artificial neural network that is used in the field of mathematical modeling. This type of network employs radial basis functions as its activation functions. The output of the network is a linear combination of the neuron parameters and the radial basis functions of the inputs to the network. Radial basis function networks have a wide range of applications, some of which include the approximation of functions, the prediction of time series, the classification of data, and the control of systems. In their study from 1988, Broomhead and Lowe, who were both researchers at the Royal Signals and Radar Establishment, were ...
Support Vector Machine
What Is Support Vector Machine In the field of machine learning, support vector machines are supervised learning models that examine data for classification and regression analysis. These models come with related learning algorithms. Vladimir Vapnik and his coworkers at AT&T Bell Laboratories were responsible for its creation. Because they are founded on statistical learning frameworks or the VC theory, which was developed by Vapnik and Chervonenkis (1974), support vector machines (SVMs) are among the most accurate prediction systems. A non-probabilistic binary linear classifier is what results when an SVM training algorithm is given a series of training examples, each of which is marked as ...
Bayesian Learning
What Is Bayesian Learning In the field of statistics, an expectation-maximization (EM) algorithm is an iterative approach to discover (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM algorithms are also known as maximum likelihood or maximum a posteriori (MAP) estimations. The expectation (E) step of the EM iteration creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and the maximization (M) step of the EM iteration computes parameters with the goal of maximizing the expected log-likelihood found on the expectatio...
Atheism, Morality, and Meaning
Defending atheism, Martin (philosophy, Boston University, emeritus) casts supernatural disbelief as the foundation for a system of value, meaning, and morality. He argues that the belief in God is superfluous, and perhaps even a hindrance, to leading a moral and purposeful life. Traditional objections to atheism are countered, Christian ethics critiqued, and an atheistic meta-ethics developed. Special scrutiny is given to the Christian doctrines of atonement, salvation, and resurrection. Annotation (c)2003 Book News, Inc., Portland, OR (booknews.com).
