When developing autonomous learning agents, the performance depends crucially on the selection of reasonable learning parameters, for example learning rates or exploration parameters. In this work we investigate meta-learning of exploration parameters by using the “REINFORCE exploration control” (REC) framework, and combine REC with replacing eligibility traces, which are a basic mechanism for tackling the problem of delayed rewards in reinforcement learning. We show empirically for a robot example and the mountain–car problem with two goals how the proposed combination can help to improve learning performance. Furthermore, we also observe that the setting of time constant λ is not straightforward, because it is intimately interrelated with the learning rate α .
This work presents a technique for the classification of emotions in human-computer interaction. Based on biophysiological data, a dictionary learning approach is used to generate sparse representations of blood volume pulse signals. Such features are then used for classification of the current emotion. Unlabeled data, i.e. data without information about class membership, is used to enrich the dictionary learning stage. Superior representation abilities of the underlying structure of the data are demonstrated by the learnt dictionaries. As a result, classification rates are improved. Experimental validation in the form of different classification experiments is presented. The results are presented with a discussion about the benefits of the approach and the existing limitations.
In many unsupervised learning problems data can be available in different representations, often referred to as views. By leveraging information from multiple views we can obtain clustering that is more robust and accurate compared to the one obtained via the individual views. We propose a novel algorithm that is based on neighborhood coregularization of the clustering hypotheses and that searches for the solution which is consistent across different views. In our empirical evaluation on publicly available datasets, the proposed method outperforms several state-of-the-art clustering algorithms. Furthermore, application of our method to recently collected biomedical data leads to new insights, critical for future research on determinants of the cervicovaginal microbiome and the cervicovaginal microbiome as a risk factor for the transmission of HIV. These insights could have an influence on the interpretation of clinical presentation of women with bacterial vaginosis and treatment decisions.
Anomaly detection is the problem of identifying objects appearing to be inconsistent with the remainder of that set of data. Detecting such samples is useful on various applications such as fault detection, fraud detection and diagnostic systems. Partially supervised methods for anomaly detection are interesting because they only need data labeled as one of the classes (normal or abnormal). In this paper, we propose a partially supervised framework for anomaly detection based on convex hulls in a parameter space, assuming a given probability distribution. It can be considered a framework since it supports any model for the “normal” samples. We investigate an algorithm based on this framework, assuming the Gaussian distribution for the not anomalous (“normal”) data, and compared the results with the One-class SVM and Naïve Bayes classifiers, as well as two statistical anomaly detectors. The proposed method shows accuracy results that are comparable or better than the competing methods. Furthermore, this approach can handle any probability distribution or mixture of distributions, allowing the user to choose a parameter space that models adequately the problem of finding anomalies.
Although the kernel-based fuzzy c-means (KFCM) algorithm utilizing a kernel-based distance measure between patterns and cluster prototypes outperforms the standard fuzzy c-means clustering for some complex distributed data, it is quite sensitive to selected kernel parameters. In this paper, we propose the stretched kernel-based fuzzy clustering method with optimized kernel parameter. The kernel parameters are updated in accordance with the gradient method to further optimize the objective function during each iteration process. To solve the local minima problem of the objective function, a function stretching technique is applied to detect the global minimum. Experiments on both synthetic and real-world datasets show that the stretched KFCM algorithm with optimized kernel parameters has better performance than other algorithms.
