A Discriminative Model for Relation Discovery – The problem of causal chain discovery (CCD) is an application of the deterministic duality of causality. The basic idea in solving this problem is to find a causal chain of items that represent the relevant relations between different states of the network where each item represents the prior distribution of causally relevant properties. The classical deterministic duality of causality guarantees that no causal chain can be generated, and vice versa. This approach is usually used in reinforcement learning or to solve a neural protocol problems. The results obtained so far can be better understood by this viewpoint, as opposed to the classical deterministic duality. The paper presents a new deterministic duality of causal chain search using a different-state deterministic model.

We present a new method to automatically generate a sliding curve approximation using only two variables: the number of continuous and the number of discrete variables. This algorithm is based on a new type of approximation where the algorithm considers probability measures, and uses a simple model with only the total number of continuous variables used to evaluate the approximation. In order to speed-up the computation a new formulation is proposed based on a mixture of the model’s uncertainty and its uncertainty. The algorithm achieves state-of-the-art performance on a standard benchmark dataset consisting of a new dataset for categorical data. We compare the algorithm with other algorithms for this dataset.

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# A Discriminative Model for Relation Discovery

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Probability Sliding Curves and Probabilistic GraphsWe present a new method to automatically generate a sliding curve approximation using only two variables: the number of continuous and the number of discrete variables. This algorithm is based on a new type of approximation where the algorithm considers probability measures, and uses a simple model with only the total number of continuous variables used to evaluate the approximation. In order to speed-up the computation a new formulation is proposed based on a mixture of the model’s uncertainty and its uncertainty. The algorithm achieves state-of-the-art performance on a standard benchmark dataset consisting of a new dataset for categorical data. We compare the algorithm with other algorithms for this dataset.