We use homological ideals in triangulated categories to get a sufficient criterion for a pair of subcategories in a triangulated category to be complementary. We apply this criterion to construct the Baum–Connes assembly map for locally compact groups and torsion-free discrete quantum groups. Our methods are related to the abstract version of the Adams spectral sequence by Brinkmann and Christe...

Our proposed method is to use a Hidden Markov Model-based word segmenter and a Support Vector Machine-based chunker for Chinese word segmentation. Firstly, input sentences are analyzed by the Hidden Markov Model-based word segmenter. The word seg-menter produces n-best word candidates together with some class information and confidence measures. Secondly, the extracted words are broken into cha...

We consider the problem of maximizing certain positive rational functions of a form that includes statistical constructs such as conditional mixture densities and conditional hidden Markov models. The wellknown Baum-Welch and expectation maximization (EM) algorithms do not apply to rational functions and are therefore limited to the simpler maximum-likelihood form of such models. Our main resul...

In this paper we address the problem of building speaker dependent Hidden Markov Models for a speaker verification system. A number of model building techniques are described and the comparative performance of a system using models built using each of these techniques is presented. Mean estimated models, models where the means of the HMMs are estimated using segmental K means but where the vari...

SUMMARY Hidden Markov models (HMMs) are widely used for biological sequence analysis because of their ability to incorporate biological information in their structure. An automatic means of optimizing the structure of HMMs would be highly desirable. However, this raises two important issues; first, the new HMMs should be biologically interpretable, and second, we need to control the complexity ...

Stochastic topological models, and hidden Markov models in particular, are a useful tool for robotic navigation planning. In previous work we have shown how weak odometric data can be used to improve learning overcoming the common problems of standard Baum-Welch algorithm. Odometric typically contain directional information, which imposes two difficulties: First, cyclicity requires use special ...

Senones were introduced to share Hidden Markov model (HMM) parameters at a sub-phonetic level in [3] and decision trees were incorporated to predict unseen phonetic contexts in [4]. In this paper, we will describe two applications of the senonic decision tree in (1) dynamically downsizing a speech recognition system for small platforms and in (2) sharing the Gaussian covariances of continuous d...

This paper describes techniques for segmentation of conversational speech based on speaker identity. Speaker seg-mentation is performed using Viterbi decoding on a hidden Markov model network consisting of interconnected speaker sub-networks. Speaker sub-networks are initialized using Baum-Welch training on data labeled by speaker, and are iteratively retrained based on the previous segmentatio...

This paper investigates a modified modeling of networked control systems (NCSs) with programmable logic controller (PLC). First, the controller-to-actuator and sensor-to-controller network-induced delays are investigated by tactics based on hierarchical coloured petri net (HCPN) in structure-conserving way. Comparing recent result, signal transmission delay is set random interval instead fixed ...

In this paper, a method for analytic handwritten word recognition based on causal Markov random fields is described. The words models are HMMs where each state corresponds to a letter; each letter is modelled by a NSHP-HMM (Markov field). Global models are build dynamically, and used for recognition and learning with the Baum-Welch algorithm. Learning of letter and word models is made using the...