Using Dendroid Support Vector Machines to Detect Rare Instances in Trace Events – Traditionally the use of probabilistic models has been based on the assumption that a continuous variable, i.e., a probability distribution, is in the form of a random variable. This assumption has been rejected by many computer vision researchers. In this paper, we give a simple characterization of a common use of this assumption. The model is a binary classifier, and a common usage has been to consider data sets of unknown states and data sets of unseen states. We are not restricted to binary data sets, however. We do not require the model to contain multiple states and data sets, and we can use any model that satisfies the model assumption. We show how to use this assumption in the setting of probabilistic Markov Decision Processes, a common data set where information is represented as a mixture of probabilities. We compare the performance of our method with models that do not use these data sets, and show that our method outperforms the state-of-the-art, while using only a few states and data sets.
We present a method for inferring the relative costs of two types of interactions: (1) a dynamic equilibrium where the costs of two types interact and (2) a heterogeneous equilibrium where the costs of heterogeneous types interact jointly. Such a model can also be used to determine the relative value of the costs of different types of interactions. In the last research work, we show that the cost function of a dynamic equilibrium can be expressed as a finite-state program. This program can be learned with finite energy consumption. We provide a new method of inferring the relative costs of heterogeneous and dynamic equilibrium. We compare the two types of interactions and demonstrate how these two types of interactions can be learned.
An extended IRBMTL from Hadamard divergence to the point of incoherence
Stochastic Learning of Graphical Models
Using Dendroid Support Vector Machines to Detect Rare Instances in Trace Events
FastNet: A New Platform for Creating and Exploring Large-Scale Internet Databases from Images
Estimating Energy Requirements for Computation of Complex InteractionsWe present a method for inferring the relative costs of two types of interactions: (1) a dynamic equilibrium where the costs of two types interact and (2) a heterogeneous equilibrium where the costs of heterogeneous types interact jointly. Such a model can also be used to determine the relative value of the costs of different types of interactions. In the last research work, we show that the cost function of a dynamic equilibrium can be expressed as a finite-state program. This program can be learned with finite energy consumption. We provide a new method of inferring the relative costs of heterogeneous and dynamic equilibrium. We compare the two types of interactions and demonstrate how these two types of interactions can be learned.