Results 151 to 160 of about 129,944 (172)
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2017
The modern learning environment (MLE) is a particular technology that serves to create an environment that will best cultivate a moral self in line with state bureaucratic needs. This chapter uncovers the genealogy of the MLE and interprets its meaning from the perspective of the classroom teacher.
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The modern learning environment (MLE) is a particular technology that serves to create an environment that will best cultivate a moral self in line with state bureaucratic needs. This chapter uncovers the genealogy of the MLE and interprets its meaning from the perspective of the classroom teacher.
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Supervised Conditional MLE-Based Learning
1999The learning scheme introduced in Chapter 3 involves a sizable number of the unknown potential values for the Gibbs models to be refined by stochastic approximation (see Table 2.4). This number can be considerably reduced by exploiting, instead of the unconditional MLE of Eq. (3.3), the conditional MLE of potentials described in this chapter.
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Supervised MLE-Based Parameter Learning
1999This chapter shows that the Gibbs models with multiple pairwise pixel interaction proposed in Chapter 2 have almost the same schemes of supervised parameter learning. The learning scheme was proposed first for homogeneous textures in (Gimel’farb, 1996a) and then generalized to piecewise‐homogeneous ones in (Gimel’farb, 1996b, 1996c).
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Conditions for Consistency of MLE’s
1994It is proved that the MLE of a deterministic signal in i. i. d. noise may be inconsistent when the noise distribution has a heavy tail. A simplified version of the necessary and sufficient condition for consistency of MLE’s found in Vajda (1993) is formulated for models with i. i. d. observations and with parameters from reasonable metric spaces. It is
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No Rose for MLE: Inadmissibility of MLE for Evaluation Aggregation Under Levels of Expertise
2022 IEEE International Symposium on Information Theory (ISIT), 2022Charvi Rastogi +3 more
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