Takeshi Asada

Stereoregular oligomers of methyl methacrylate

SummaryIsotactic (it-) and syndiotactic (st-) MMA oligomers from 19-mer to 29-mer could be isolated efficiently from it-PMMA (

Stereospecific synthesis of ditactic polymers

\overline {DP}

Support Vector Machines using Multi Objective Programming

= 28.6) and st-PMMA (

Stereoregular oligomers of methyl methacrylate: 6. Isolation of isotactic and syndiotactic MMA oligomers from 19-mer to 29-mer by preparative supercritical fluid chromatography and their thermal analysis

\overline {DP}

Highly stereospecific protonation of stereoregular living PMMA anions with several alcohols

= 28.6) (sample load: 50 mg) by the SFC using a 10 mm i. d. x 250 mm column packed with silica gel. DP of each isolated oligomer was determined by FD mass spectroscopy, and the values agreed well with those calculated from the relative intensity of 1H NMR signals due to CH3O- and the terminal t-C4H9-groups. Glass transition temperature (Tg) of the it-28-mer measured by DSC was 34.5°C, which was higher than that of the it-PMMA by 6.5°C. Tg of both the it-and st-oligomers increased linearly with DP in the range of DP=20∼29. A 1: 1 mixture of the it- and st-27-mers annealed at 140°C showed an endothermic transition at 102.3°C which was attributable to melting of stereocomplex, whereas an annealed 1: 1 mixture of the it- and st-PMMAs had a much broader endotherm around 80∼140°C.

Highly diheterotactic polymerization of tert-butyl crotonate

Abstract Techniques for machine learning have been extensively studied in recent years as effective tools in data mining. Although there have been several approaches to machine learning, we focus on the mathematical programming (in particular, multi-objective and goal programming; MOP/GP) approaches in this paper. Among them, Support Vector Machine (SVM) is gaining much popularity recently. In pattern classification problems with two class sets, its idea is to find a maximal margin separating hyperplane which gives the greatest separation between the classes in a high dimensional feature space. This task is performed by solving a quadratic programming problem in a traditional formulation, and can be reduced to solving a linear programming in another formulation. However, the idea of maximal margin separation is not quite new: in the 1960s the multi-surface method (MSM) was suggested by Mangasarian. In the 1980s, linear classifiers using goal programming were developed extensively. This paper presents an overview on how effectively MOP/GP techniques can be applied to machine learning such as SVM, and discusses their problems.