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The recent success of AlphaGO has shown that it is possible to combine machine learning with Monte Carlo Tree Search (MCTS) in order to improve performance in games with large branching factors. This paper explores the question of whether similar ideas can be applied to a genre of games with an even larger branching factor: Real-Time Strategy games. Specifically, this paper studies (1) the use of...
In this work we address the multispectral image classification problem from a Bayesian perspective. We develop an algorithm which utilizes the logistic regression function as the observation model in a probabilistic framework, Super-Gaussian (SG) priors which promote sparsity on the adaptive coefficients, and Variational inference to obtain estimates of all the model unknowns. The proposed algorithm...
This paper proposes an extended Constrained Local Model (CLM) formulation for aligning faces using depth information. The CLMs are popular methods that were initially designed to locate facial features in regular intensity images. Briefly, they combine a set of local detectors, one for each landmark, whose locations are regularized by a linear shape model. Fitting a CLM is usually framed as a two...
In this paper, we propose a novel gaze estimation method to evaluate the attention span of users upon on-screen content via a single webcam. Our method is based on supervised descent method for eye region of interest (ROI) extraction. Then, boost Gaussian Bayesian regressors are applied to learn a robust mapping from the input eye ROI to gaze coordinates. To get enough training samples, we implant...
In order to improve the quality of graduate dissertation and examine the quality of graduate education, the mechanism of graduate dissertation random inspection evaluation in Shanghai has been operated for more than ten years. Evaluation experts evaluate the quality of dissertation by using subitem evaluation method rather than comprehensive evaluation method to reduce the risk of misjudgment. Decision...
In this paper, we present a detailed comparison study of skin segmentation methods for psoriasis images. Different techniques are modified and then applied to a set of psoriasis images acquired from the Royal Melbourne Hospital, Melbourne, Australia, with aim of finding the best technique suited for application to psoriasis images. We investigate the effect of different colour transformations on skin...
Bayesian network (BN), an important machine learning technique, has been widely used in modeling relationships among random variables. BN is considered to be suitable for tasks like prediction, classification and cause analysis. In fact, Bayesian network model often preforms better precision than other commonly used algorithm models in classification and prediction. Meanwhile, taking Max-Min-Hill-Climbing...
We review recent theoretical results in maximum entropy (MaxEnt) PDF projection that provide a theoretical framework for fusing the information from multiple features for the purpose of general statistical inference. Given a high-dimensional input data vector x, and several dimension-reducing feature transformations zi = Ti(x), we consider the problem of estimating the probability density function...
Classifier fusion is a classical approach to improve the classification accuracy. The multiple classifiers to combine have in general different classification qualities (i.e. performances), and the proper evaluation of the classifier quality plays an important role for achieving the best global performance. We propose a new method for classifier fusion based on refined reliability evaluation (CF-RRE)...
In this paper, we propose techniques for detecting anomalies in user accesses by learning profiles of normal access patterns of users based on both the syntactic and semantic features of past users queries stored in database logs. New accesses are checked upon these profiles and deviations are considered anomalous accesses which may be indications of potential insider attacks. We consider two scenarios...
Exploiting both labeled and unlabeled instances of various problems seems a really promising strategy, since useful information that is contained on the latter pool of data is discarded during supervised approaches. However, the size of the unlabeled data that needs to be examined is usually extremely large and efficient algorithms should be utilized in such cases. Hidden Naive Bayes (HNB) model constitutes...
Statistical and machine learning methods have been proposed to predict hard drive failure based on SMART attributes, and many achieve good performance. However, these models do not give a good indication as to when a drive will fail, only predicting that it will fail. To this end, we propose a new notion of a drive's health degree based on the remaining working time of hard drive before actual failure...
Traditional Support Vector Regression (SVR) solvers require user pre-specified penalty (regularization) parameter as input and typically model the training data with maximum a posterior (MAP) principle. The resultant point estimates can be affected seriously by inappropriate regularization, outliers and noise, especially when training online. In this paper, we address the aforementioned problems by...
Hyperspectral images in the thermal infrared range are attracting increasing attention in the remote sensing field. Nonetheless, the generation of land cover maps using this innovative kind of remote sensing data has been scarcely studied so far. The aim of this article is to experimentally investigate the potential of various supervised classification approaches to land cover mapping from high spatial...
Conditional Random Fields (CRF), a structured prediction method, combines probabilistic graphical models and discriminative classification techniques in order to predict class labels in sequence recognition problems. Its extension the Hidden Conditional Random Fields (HCRF) uses hidden state variables in order to capture intermediate structures. The number of hidden states in an HCRF must be specified...
Over the past few years extensive research has been conducted to solve classification problems with help of machine learning techniques. However, machine learning is data-driven and obtaining labeled data is often challenging in real applications. Techniques that try to overcome this burden, especially, in the presence of sparsely labeled data, can be found in the field of semi-supervised or active...
The classification of high dimensional data is an arduous task especially with the emergence of high quality data acquisition techniques. This problem is accentuated when the whole set of features is needed to learn a classifier such as the case of genomic data. The Bayesian approach is suitable for these applications because it represents graphically and statistically the dependencies between the...
Model complexity of Genetic Programming (GP) as a learning machine is currently attracting considerable interest from the research community. Here we provide an up-to-date overview of the research concerning complexity measure techniques in GP learning. The scope of this review includes methods based on information theory techniques, such as the Akaike Information Criterion (AIC), Bayesian Information...
Off-the-shelf Gaussian Process (GP) covariance functions encode smoothness assumptions on the structure of the function to be modeled. To model complex and non-differentiable functions, these smoothness assumptions are often too restrictive. One way to alleviate this limitation is to find a different representation of the data by introducing a feature space. This feature space is often learned in...
In this paper, we proposed the human action recognition method using the variational Bayesian HMM with Gaussian — Wishart emission mixture model. First, we defined the Bayesian HMM based on a finite number of Gaussian-Wishart mixture components to support continuous emission observations. Second, we have considered a variational Bayesian inference method to derive the posterior distributions for hidden...
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