Decision intelligenceDecision intelligence is an engineering discipline that augments data science with theory from social science, decision theory, and managerial science. Its application provides a framework for best practices in organizational decision-making and processes for applying machine learning at scale. The basic idea is that decisions are based on our understanding of how actions lead to outcomes. Decision intelligence is a discipline for analyzing this chain of cause and effect, and decision modeling is a visual language for representing these chains.
Complete (complexity)In computational complexity theory, a computational problem is complete for a complexity class if it is, in a technical sense, among the "hardest" (or "most expressive") problems in the complexity class. More formally, a problem p is called hard for a complexity class C under a given type of reduction if there exists a reduction (of the given type) from any problem in C to p. If a problem is both hard for the class and a member of the class, it is complete for that class (for that type of reduction).
Early stoppingIn machine learning, early stopping is a form of regularization used to avoid overfitting when training a learner with an iterative method, such as gradient descent. Such methods update the learner so as to make it better fit the training data with each iteration. Up to a point, this improves the learner's performance on data outside of the training set. Past that point, however, improving the learner's fit to the training data comes at the expense of increased generalization error.
Log-space reductionIn computational complexity theory, a log-space reduction is a reduction computable by a deterministic Turing machine using logarithmic space. Conceptually, this means it can keep a constant number of pointers into the input, along with a logarithmic number of fixed-size integers. It is possible that such a machine may not have space to write down its own output, so the only requirement is that any given bit of the output be computable in log-space. Formally, this reduction is executed via a log-space transducer.
Decision stumpA decision stump is a machine learning model consisting of a one-level decision tree. That is, it is a decision tree with one internal node (the root) which is immediately connected to the terminal nodes (its leaves). A decision stump makes a prediction based on the value of just a single input feature. Sometimes they are also called 1-rules. Depending on the type of the input feature, several variations are possible.
Lattice-based access controlIn computer security, lattice-based access control (LBAC) is a complex access control model based on the interaction between any combination of objects (such as resources, computers, and applications) and subjects (such as individuals, groups or organizations). In this type of label-based mandatory access control model, a lattice is used to define the levels of security that an object may have and that a subject may have access to. The subject is only allowed to access an object if the security level of the subject is greater than or equal to that of the object.
Organisation-based access controlIn computer security, organization-based access control (OrBAC) is an access control model first presented in 2003. The current approaches of the access control rest on the three entities (subject, action, object) to control the access the policy specifies that some subject has the permission to realize some action on some object. OrBAC allows the policy designer to define a security policy independently of the implementation. The chosen method to fulfill this goal is the introduction of an abstract level.
RSBACRule-set-based access control (RSBAC) is an open source access control framework for current Linux kernels, which has been in stable production use since January 2000 (version 1.0.9a). Free open source GNU General Public License (GPL) Linux kernel security extension Independent of governments and big companies Several well-known and new security models, e.g.
