We present several new algorithms for bidirectional best-first search that employ a front-to-front strategy of estimating distances from newly-generated frontier nodes in one search direction to existing frontier nodes in the other search direction, rather than estimating distances to terminal nodes in both searches. Unlike previous front-to-front strategies that use a shared priority queue to manage both frontiers, we use a separate data structure for each search, and choose that data structure to minimize the amount of computational effort required by the best-first search algorithm it supports. We demonstrate several results. First, we show that Bidirectional Front-to-Front Greedy (BFFG) is able to quickly find sub-optimal solutions to very large statespace problems and with a small fraction of nodes expanded (and stored) compared to other unidirectional and bidirectional greedy techniques. Secondly, we show that Bidirectional Front-to-Front A* (BFFA*) similarly outperforms both Unidirectional A* and Bidirectional Front-to-End A* (BFEA*) in terms of node expansions when searching for optimal solutions. Finally, we describe three improvements to BFFA*, each of which reduces the overall runtime by limiting the number of opposing frontier nodes that need be considered while preserving the optimality criterion.

This paper examines the effect of providing adversarial labels to several algorithms that use noisy labels from multiple experts to estimate classifier accuracy, referred to hereafter as "estimators." We propose four adversary labeling strategies and use experiments on synthetic data to gauge their impact on the estimators. Our results show that even a single adversary can considerably impact the effectiveness of an estimator. In addition, we find that estimators that weight the input of all experts equally tend to be much more affected by the inclusion of adversaries than those that can separately model each expert and that the impact of adversaries is lessened when the experts have higher accuracy.

Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this article, we model a sequence of credit card transactions from three different perspectives, namely (i) does the sequence contain a Fraud? (ii) Is the sequence obtained by fixing the card-holder or the payment terminal? (iii) Is it a sequence of spent amount or of elapsed time between the current and previous transactions? Combinations of the three binary perspectives give eight sets of sequences from the (training) set of transactions. Each one of these sets is modelled with a Hidden Markov Model (HMM). Each HMM associates a likelihood to a transaction given its sequence of previous transactions. These likelihoods are used as additional features in a Random Forest classifier for fraud detection. This multiple perspectives HMM-based approach enables an automatic feature engineering in order to model the sequential properties of the dataset with respect to the classification task. This strategy allows for a 15% increase in the precision-recall AUC compared to the state of the art feature engineering strategy for credit card fraud detection.

Thanks to digitization of industrial assets in fleets, the ambitious goal of transferring fault diagnosis models fromone machine to the other has raised great interest. Solving these domain adaptive transfer learning tasks has the potential to save large efforts on manually labeling data and modifying models for new machines in the same fleet. Although data-driven methods have shown great potential in fault diagnosis applications, their ability to generalize on new machines and new working conditions are limited because of their tendency to overfit to the training set in reality. One promising solution to this problem is to use domain adaptation techniques. It aims to improve model performance on the target new machine. Inspired by its successful implementation in computer vision, we introduced Domain-Adversarial Neural Networks (DANN) to our context, along with two other popular methods existing in previous fault diagnosis research. We then carefully justify the applicability of these methods in realistic fault diagnosis settings, and offer a unified experimental protocol for a fair comparison between domain adaptation methods for fault diagnosis problems.

Rewards in Markov Decision Processes (MDP) define the behavior of the model. Without a clear interpretation of what the reward function is and is not capturing, one cannot trust their model nor diagnose when the model is giving incorrect recommendations. Increasing complexity of state-of-the-art models used to represent the reward function and model-free methods that attempt to avoid representing this function make trusting the model much more difficult. We map these reward functions onto a standard classification problem where we can explain what factors the model considers in making decisions in local and global contexts and quantify whether the fit of the reward function is likely to be good for explaining the behavior of the model. We evaluate our proof-of-concept on both the standard version and a modified version of the Object World domain to add more nonlinearity.

For example, while a recommendation of their behavior such as browsing trails, bookmarks ratings, source based on co-authorship links ranks attendees or created social links. It enables modern recommender systems by its social similarity with the target user, the recommendation to use multiple sources of information about user interests case might require to find attendees who are interested and preferences to deliver better recommendations. This in similar topics while being most likely unknown is most frequently done using parallel hybrid recommendation to the target user (i.e., having the weakest social similarity).

Deep convolutional sum-product networks (DCSPNs) have very recently been introduced and shown to yield state-of-the-art results in image completion tasks. A DCSPN consists of a tree structure (a directed acyclic graph) coupled with parameters of the structure. Given that DCSPNs are in their infancy, many open questions remain regarding the properties and topology of its tree structure. In this paper, we undertake three investigations pertaining to the DCSPN structure. The first two studies revolve around the original structure put forth in the seminal paper. These studies increase the number of pooling layers and vary the hyperparameters in attempts to improve accuracy. The third inquiry suggests a new DCSPN tree structure that significantly lowers the training time at some modest expense of accuracy.

In this work is proposed a method for Hierarchical Classification, which takes advantage of the hierarchical structure to influence the prediction of local classifiers with their neighbors. To achieve this, two strategies are combined. The first is to represent the hierarchical structure as a Bayesian network, and the second is to build chained classifiers that feed the Bayesian network as local classifiers. The proposed method was tested in several datasets of functional genomics, which consist of tree-structured hierarchies. The results of several variants of the proposed method are compared to the standard methods, Flat and Top-Down, as well as with a start of the art technique, showing superior performance under several metrics.

Halpern 1990; Smets 1991; Yu and Arasta 1994), Dempster's conditional and Fagin-Halpern (FH) conditional can be considered the most widely used two DST conditional The flexibility and expressiveness of Dempster-Shafer (DS) A widely used approach for carrying out precise computation theoretic models make DS evidence theory (Dempster 1967; of the Dempster's conditional is a matrix calculus 1968; Shafer 1976) an ideal framework for reasoning and based algorithm which generates the Dempster's conditional decision making under uncertainty in Artificial Intelligence masses (Klawonn and Smets 1992; Smets 2002). Therefore, this specialization matrix-based method imposes Computing the DST belief functions and the DST conditionals, a prohibitive burden when dealing with larger FoDs.

Forgetting as a knowledge management operation has received much less attention than operations like inference, or revision. It was mainly in the area of logic programming that techniques and axiomatic properties have been studied systematically. However, at least from a cognitive view, forgetting plays an important role in restructuring and reorganizing a human's mind, and it is closely related to notions like relevance and independence which are crucial to knowledge representation and reasoning. In this paper, we propose axiomatic properties of (intentional) forgetting for general epistemic frameworks which are inspired by those for logic programming, and we evaluate various forgetting operations which have been proposed recently by Beierle et al. according to them. The general aim of this paper is to advance formal studies of (intentional) forgetting operators while capturing the many facets of forgetting in a unifying framework in which different forgetting operators can be contrasted and distinguished by means of formal properties.