Despite the availability and ease of collecting a large amount of free, unlabeled data, the expensive and time-consuming labeling process is still an obstacle to labeling a sufficient amount of training data, which is essential for building supervised learning models. Here, with low labeling cost, the active learning (AL) technique could be a solution, whereby a few, high-quality data points are queried by searching for the most informative and representative points within the instance space. This strategy ensures high generalizability across the space and improves classification performance on data we have never seen before. In this paper, we provide a survey of recent studies on active learning in the context of classification. This survey starts with an introduction to the theoretical background of the AL technique, AL scenarios, AL components supported with visual explanations, and illustrative examples to explain how AL simply works and the benefits of using AL. In addition to an overview of the query strategies for the classification scenarios, this survey provides a high-level summary to explain various practical challenges with AL in real-world settings; it also explains how AL can be combined with various research areas. Finally, the most commonly used AL software packages and experimental evaluation metrics with AL are also discussed.

It is controversially discussed whether or not mood-congruent recall (i.e., superior recall for mood-congruent material) reflects memory encoding processes or reduces to processes during retrieval. We therefore investigated the neurophysiological correlates of mood-dependent memory during emotional word encoding. Event-related potentials (ERPs) were recorded while participants in good or bad mood states encoded words of positive and negative valence. Words were either complete or had to be generated from fragments. Participants had to memorize words for subsequent recall. Mood-congruent recall tended to be largest in good mood for generated words. Starting at 200 ms, mood-congruent ERP effects of word valence were obtained in good, but not in bad mood. Only for good mood, source analysis revealed valence-related activity in ventral temporal cortex and for generated words also in prefrontal cortex. These areas are known to be involved in semantic processing. Our findings are consistent with the view that mood-congruent recall depends on the activation of mood-congruent semantic knowledge during encoding. Incoming stimuli are more readily transformed according to stored knowledge structures in good mood particularly during generative encoding tasks. The present results therefore show that mood-congruent memory originates already during encoding and cannot be reduced to strategic processes during retrieval.

“Principal Component Analysis” (PCA) is an established linear technique for dimensionality reduction. It performs an orthonormal transformation to replace possibly correlated variables with a smaller set of linearly independent variables, the so-called principal components, which capture a large portion of the data variance. The problem of finding the optimal number of principal components has been widely studied for offline PCA. However, when working with streaming data, the optimal number changes continuously. This requires to update both the principal components and the dimensionality in every timestep. While the continuous update of the principal components is widely studied, the available algorithms for dimensionality adjustment are limited to an increment of one in neural network-based and incremental PCA. Therefore, existing approaches cannot account for abrupt changes in the presented data. The contribution of this work is to enable in neural network-based PCA the continuous dimensionality adjustment by an arbitrary number without the necessity to learn all principal components. A novel algorithm is presented that utilizes several PCA characteristics to adaptivly update the optimal number of principal components for neural network-based PCA. A precise estimation of the required dimensionality reduces the computational effort while ensuring that the desired amount of variance is kept. The computational complexity of the proposed algorithm is investigated and it is benchmarked in an experimental study against other neural network-based and incremental PCA approaches where it produces highly competitive results.

We show that simple perceptual competences can emerge from an internal simulation of action effects and are thus grounded in behavior. A simulated agent learns to distinguish between dead ends and corridors without the necessity to represent these concepts in the sensory domain. Initially, the agent is only endowed with a simple value system and the means to extract low‐level features from an image. In the interaction with the environment, it acquires a visuo‐tactile forward model that allows the agent to predict how the visual input is changing under its movements, and whether movements will lead to a collision. From short‐term predictions based on the forward model, the agent learns an inverse model. The inverse model in turn produces suggestions about which actions should be simulated in long‐term predictions, and long‐term predictions eventually give rise to the perceptual ability.