We consider a model of learning and evolution in games whose action sets are endowed with a partition-based similarity structure intended to capture exogenous similarities between strategies. In this model, revising agents have a higher probability of comparing their current strategy with other strategies that they deem similar, and they switch to the observed strategy with probability proportional to its payoff excess. Because of this implicit bias toward similar strategies, the resulting dynamics - which we call the nested replicator dynamics - do not satisfy any of the standard monotonicity postulates for imitative game dynamics; nonetheless, we show that they retain the main long-run rationality properties of the replicator dynamics, albeit at quantitatively different rates. We also show that the induced dynamics can be viewed as a stimulus-response model in the spirit of Erev & Roth (1998), with choice probabilities given by the nested logit choice rule of Ben-Akiva (1973) and McFadden (1978). This result generalizes an existing relation between the replicator dynamics and the exponential weights algorithm in online learning, and provides an additional layer of interpretation to our analysis and results.

Anyone who's taken a photo with a digital camera is likely familiar with a "noisy" image: discolored spots that make the photo lose clarity and sharpness. Many photographers have tips and tricks to reduce noise in images, including fixing the settings on the camera lens or taking photos in different lighting. But it isn't just photographs that can look discolored -- noise is common in computer graphics, too. Noise refers to the random variations of brightness and color that aren't part of the original image. Removing noise from imagery -- which is becoming more common in the field of image processing and computer vision -- is known as denoising.

In spite of the recent advancements being made in speech recognition, recognition errors are unavoidable in continuous speech recognition. In this paper, we focus on a word-error correction system for continuous speech recognition using confusion networks.Conventional N-gram correction is widely used; however, the performance degrades due to the fact that the N-gram approach cannot measure information between long distance words. In order to improve the performance of the N-gram model, we employ Normalized Relevance Distance (NRD) as a measure for semantic similarity between words. NRD can identify not only co-occurrence but also the correlation of importance of the terms in documents. Even if the words are located far from each other, NRD can estimate the semantic similarity between the words. The effectiveness of our method was evaluated in continuous speech recognition tasks for multiple test speakers. Experimental results show that our error-correction method is the most effective approach as compared to the methods using other features.

In spite of the recent advancements being made in speech recognition, recognition errors are unavoidable in continuous speech recognition. In this paper, we focus on a word-error correction system for continuous speech recognition using confusion networks.Conventional N-gram correction is widely used; however, the performance degrades due to the fact that the N-gram approach cannot measure information between long distance words. In order to improve the performance of the N-gram model, we employ Normalized Relevance Distance (NRD) as a measure for semantic similarity between words. NRD can identify not only co-occurrence but also the correlation of importance of the terms in documents. Even if the words are located far from each other, NRD can estimate the semantic similarity between the words. The effectiveness of our method was evaluated in continuous speech recognition tasks for multiple test speakers. Experimental results show that our error-correction method is the most effective approach as compared to the methods using other features.