Named Entity Recognition and Classification (NERC) google
The term ‘Named Entity’, now widely used in Natural Language Processing, was coined for the Sixth Message Understanding Conference (MUC-6) (R. Grishman & Sundheim 1996). At that time, MUC was focusing on Information Extraction (IE) tasks where structured information of company activities and defense related activities is extracted from unstructured text, such as newspaper articles. In defining the task, people noticed that it is essential to recognize information units like names, including person, organization and location names, and numeric expressions including time, date, money and percent expressions. Identifying references to these entities in text was recognized as one of the important sub-tasks of IE and was called ‘Named Entity Recognition and Classification (NERC)’. …

AIXIjs google
Reinforcement learning is a general and powerful framework with which to study and implement artificial intelligence. Recent advances in deep learning have enabled RL algorithms to achieve impressive performance in restricted domains such as playing Atari video games (Mnih et al., 2015) and, recently, the board game Go (Silver et al., 2016). However, we are still far from constructing a generally intelligent agent. Many of the obstacles and open questions are conceptual: What does it mean to be intelligent? How does one explore and learn optimally in general, unknown environments? What, in fact, does it mean to be optimal in the general sense? The universal Bayesian agent AIXI (Hutter, 2005) is a model of a maximally intelligent agent, and plays a central role in the sub-field of general reinforcement learning (GRL). Recently, AIXI has been shown to be flawed in important ways; it doesn’t explore enough to be asymptotically optimal (Orseau, 2010), and it can perform poorly with certain priors (Leike and Hutter, 2015). Several variants of AIXI have been proposed to attempt to address these shortfalls: among them are entropy-seeking agents (Orseau, 2011), knowledge-seeking agents (Orseau et al., 2013), Bayes with bursts of exploration (Lattimore, 2013), MDL agents (Leike, 2016a), Thompson sampling (Leike et al., 2016), and optimism (Sunehag and Hutter, 2015). We present AIXIjs, a JavaScript implementation of these GRL agents. This implementation is accompanied by a framework for running experiments against various environments, similar to OpenAI Gym (Brockman et al., 2016), and a suite of interactive demos that explore different properties of the agents, similar to REINFORCEjs (Karpathy, 2015). We use AIXIjs to present numerous experiments illustrating fundamental properties of, and differences between, these agents. …

Tree Structured Vector Quantization (TSVQ) google
1. First we apply k-means to get 2 centroids or prototypes within the entire data set. This provides us with a boundary between the two clusters, which would be a straight line based on the nearest neighbor rule.
2. Next, the data are assigned to the 2 centroids.
3. Then, for the data assigned to each centroid (call them a group), apply 2 centroid k-means to each group separately. The initialization can be done by splitting the centroid into two. Note that data points channeled to different centroids are treated separately.
4. Repeat the above step. …

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