Deep Deterministic Policy Gradient (DDPG)
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving. Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies ‘end-to-end’: directly from raw pixel inputs. …

One-Class Classification
This paper presents a method called One-class Classification using Length statistics of Emerging Patterns Plus (OCLEP+). …

Random Forest
Random forests are an ensemble learning method for classification (and regression) that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes output by individual trees. The algorithm for inducing a random forest was developed by Leo Breiman and Adele Cutler, and “Random Forests” is their trademark. The term came from random decision forests that was first proposed by Tin Kam Ho of Bell Labs in 1995. The method combines Breiman’s “bagging” idea and the random selection of features, introduced independently by Ho and Amit and Geman in order to construct a collection of decision trees with controlled variance. The selection of a random subset of features is an example of the random subspace method, which, in Ho’s formulation, is a way to implement classification proposed by Eugene Kleinberg. …

Total Unduplicated Reach and Frequency (TURF)
TURF Analysis, an acronym for “Total Unduplicated Reach and Frequency”, is a type of statistical analysis used for providing estimates of media or market potential and devising optimal communication and placement strategies given limited resources. TURF analysis identifies the number of users reached by a communication, and how often they are reached. Although originally used by media schedulers to maximize reach and frequency of media spending across different items (print, broadcast, etc.), TURF is also now used to provide estimates of market potential. For example, if a company plans to market a new yogurt, they may consider launching ten possible flavors, but in reality, only three might be purchased in large quantities. The TURF algorithm identifies the optimal product line to maximize the total number of consumers who will purchase at least one SKU. Typically, when T.U.R.F. is undertaken for optimizing a product range, the analysis only looks at the reach of the product range (ignoring the Frequency component of TURF). …