We review the literature on semi-supervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semi-supervised learning. This document is a chapter excerpt from the author’s doctoral thesis (Zhu, 2005). However the author plans to update the online version frequently to incorporate the latest development in the field. Please obtain the latest version at http://www.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf

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This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensor-based planning, visibility, decision-theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning.

manual parameter tuning than most current GMM/HMM based agglomerative clustering techniques and is more accurate at the same time. The thesis reports on empirical research to estimate the importance of each of the parameters of an agglomerative-hierarchical-clustering-based Speaker Diarization system

This paper describes the LIMSI speaker diarization system used in the RT-04F evaluation. The RT-04F system builds upon the LIMSI baseline data partitioner, which is used in the broadcast news transcription system. This partitioner provides a high cluster purity but has a tendency to split the data

We present a new self-organizing neural network model having two variants. The first variant performs unsupervised learning and can be used for data visualization, clustering, and vector quantization. The main advantage over existing approaches, e.g., the Kohonen feature map, is the ability

(Top Left) A visualization of the first two principal components of the i-vectors in a three-speaker conversation. The rest of the plots show the result of VBEM-GMM clustering after a single iteration (top right), three iterations (bottom right), and the final results (bottom left).

is the process of partitioning an input audio stream into homogeneous regions according to their specific audio sources. These sources can include audio type (speech, music, background noise, ect.), speaker identity and channel characteristics. With the continually increasing number of larges

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We present a study on the contributions to Diarization Error Rate by the various components of speaker diarization system. Following on from an earlier study by Huijbregts and Wooters, we extend into more areas and draw somewhat different conclusions. From a series of experiments combining real