Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence

Learning convergence of CMAC technique

CMAC is one useful learning technique that was developed two decades ago but yet lacks adequate theoretical foundation. Most past studies focused on development of algorithms, improvement of the CMAC structure, and applications. Given a learning problem, very little about the CMAC learning behavior such as the convergence characteristics, effects of hash mapping, effects of memory size, the err...

Extending the CMAC model: adaptive input quantization

Hierarchically Clustered LSH for Hierarchical Outliers Detection

In this work we introduce hierarchical outliers that extend the notion of distance-based outliers for handling hierarchical data domains. We present a novel framework that permits us to detect hierarchical outliers in a consistent manner, providing a desired monotonicity property, which implies that a data observation that finds enough support so as to be disregarded as an outlier at a level of...

Adaptive Vector Quantization for Reinforcement Learning

Dynamic programming methods are capable of solving reinforcement learning problems, in which an agent must improve its behavior through trial-and-error interactions with a dynamic environment. However, these computational algorithms suffer from the curse of dimensionality (Bellman, 1957) that the number of computational operations increases exponentially with the cardinality of the state space....

Adaptive Relevance Matrices in Learning Vector Quantization

We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive metric. By introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme can be taken into account and automated, and ...