vector rotation about an axis

... mor- bidity among Rwandan refugees repatriated from Zaire, November, 1996. Prehospital Disaster Med 1998, 13:17-21. 11. Morbidity and mortality surveillance in Rwandan refugees – Burundi and Zaire, 1994. ... validity of the study and the applicability of the findings to their settings. Public health scenario You are a physician working for an international human- itarian medical organization as head ... Surveillance of mortality during a refugee crisis – Guinea, January–May 2001. MMWR Morb Mortal Wkly Rep 2001, 50:1029-32. 3. The Standardized Monitoring and Assessment of Relief and Transition...

... position at any instant (measured by a high- resolution position sensor, or estimated by an observer) is the mandatory requirement in the high-performance vector control method. Vector control ... permanent magnet (PM) materials, PM (brushless) motors have become more and more popular and find their applications in a wide range of fields: industry, office use machines, house appliances, ... PVC has 2 majors advantages: a) the torque ripple is greatly reduced, and b) the flux weakening for constant-power high-speed mode can be achieved by injecting a negative d -axis current into...

... is announced as the reincarnation of a Tibetan Lama. This leader reports, furthermore, that he has witnessed an eastern sage grow an orange tree from his palm and, on another occasion, die and ... Dr. R. H. Chaney, Dr. W. J. Cranston, Dr. H. T. Harper, Dr. Lansing Lee, and Dr. J. D. Gray. The interest and understanding shown by these and others in the problems of the newly organized full-time ... through man's Odyssey: Yet, strangely, man sometimes will find his own. And even man has felt the arcane flow Whence brims unchanged the very Attic wine, Where lives that mute and death-eclipsing...

... SECURITY POPULATION AND AGING PUBLIC SAFETY SCIENCE AND TECHNOLOGY SUBSTANCE ABUSE TERRORISM AND HOMELAND SECURITY TRANSPORTATION AND INFRASTRUCTURE WORKFORCE AND WORKPLACE The RAND Corporation ... International Policy Analyst RAND Corporation Michael Spirtas, Ph.D. Policy Analyst RAND Corporation The RAND Corporation is a nonprofit research organization providing objective analysis and effective ... in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from RAND. Published 2008 by the RAND...

... group-guarantees, the price of the Grameen Bank loan, the bank’s reliability, and its scale. Like the Village Banks, Grameen insists that the clients which it 33 Grameen Bank is structured as a bank ... basic personal financial intermediation. With the urban moneylender, the pay-out comes first, and can be understood as an advance against future savings. Indeed, very many loans to poor people ... hook, and read on. David Hulme Manchester February 1999 i The Poor and Their Money An essay about financial services for poor people Stuart Rutherford Institute for Development Policy and Management University...

... a kanji character and a hi- ragana character. Of course, there are quite a few exceptions to this heuristics. For example, some proper nouns are written in mixed hiragana, kanji and katakana. The ... 208,000 sentences. We se- lected randomly 20,000 sentences for training and 4 Hiragana and katakana are phonetic characters which rep- resent Japanese syllables. Katakana is primarily used to write foreign ... sup- port vector machines: Learning with many relevant features. In Proceedings of the European Conference on Machine Learning. TakuKudo and Yuji Matsumoto. 2000a. Japanese depen- dency structure analysis...

... had to do. And the man told about Humpy-Dumpy that tumbled down, who notwithstanding came to the throne, and at last married the princess. And the children clapped their hands, and cried. ... than a yard from the land, and was gliding quickly onward. Little Gerda was very frightened, and began to cry; but no one heard her except the sparrows, and they could not carry her to land; ... large as a grain of sand, and they ew about in the wide world, and when they got into people’s eyes, there they stayed; and then people saw everything perverted, or only had an eye for that which...

... param- eters (Purandare and Pedersen, 2004; Kulkarni and Pedersen, 2005). 4 Our Approach Our approach has three stages: i) we create a the feature vector for the target word (instance vector) and each ... concepts (concept vectors) using SenseClusters, ii) we calculate the cosine be- tween the instance vector and each of the concept vectors, and iii) we assign the concept whose con- cept vector is the ... the instance vector and each of the two concept vectors. The concept whose vector has the smallest cosine score is assigned to weight. We then repeat this process for f luctuated, past and months. We...

... akYkXk (I10) (13) in formula (10), xk represents support vector, and k is Finally we obtain the following decision function: the number of support vector. fk(x) =sgntRk -{K(x,x) +2 E a,y,K(x,X) -ZE a,ayjy,yjK(x,ix)} If f(x) > 0, the tested sample is contained in sphere, ,ESV ,ESV and we look the samples enclosed I sphere the same-class sgn{R21 + 2Rkl E aoy1xi +( E aciyiXi)2} objects. Otherwise it is rejected, and we look it as the Xi,SVk xi,SVk opposite objects. -{K(x, x) + 2 E a1yiK(x, xi)-E aa1jy1yjK(x , xj)} xi ESV xiESV 3. SVDC Incremental Learning Algorithm According formula (6), we suppose the obtained initial sgn{ffk (x) + 2Rk L E aiy,x, + ( a ciyixi)2} parameter (sphere radius) learning with initial training set is xi csVk xi csVk RO, and the set of support vectors is SVO . The parameter (14) From equation (14) we can see it is easy to return the becomes Rk in the kth incremental learning, and the set last step of incremental earning without extra computation. of support vectors becomes SVk, and the new dataset in From the above analysis we can see only conduct a trifling modification on the standard SVDC, can it be used klh step becomes Dk = {(xk yk)j}l- to solve the updated model in incremental learning procedure. Our incremental algorithm can be described as Nowwesummarizeouralgorithmasfollowings: following: Step 1 Learning the initial concept: training SVDC Assume we has known Rkl updating the current using initial datasetoTS , then parameter R0 is model~~~~ ~ ~ usn SVkn lnXka daae TSo I/hnpaaeerR model using SJK,l1 and new dataset {(X iY7)}>=1 obtained; We updating the current model using the following Step 2 Updating the current concept: when the new data are available, using them to solve QP problem quadratic programming (QP) problem: formula ( 11), and obtain new concept; min g(Rk) I Rk - R 112 Step 3 Repeating step 2 until the incremental learning is k (Rk2 _(Xk - a)' (XV -a)) > Xk exi Dk over where Rk-l is the radius of last optimization problem (11), 4. Experiments and Results when k = 1, Ro is the radius of standard SVDC. It is In order to evaluate the learning performance offered by obvious, when RklI = 0, the incremental SVDC has the our incremental algorithm, we conducted experiment on six different datasets taken from UCI Machine Repository: same form as the standard SVDC. We will found the Banana, Diabetes, Flare-Solar, Heart, Breast-Cancer, German. updated model by the incremental SVDC also owns the Note some of then are not binary -class classification problems, but we have transform them to binary-class problem by special property of solution sparsity which is owned by the technique. Experiment parameters and Dataset are shown in standard SVDC ... 9 10 [6] L. Baoqing. Distance-based selection of potential support vector Incremental Learning Step by kernel matrix. In International symposium on Neural (f) Networks 2004, LNCS 3173,pp. 468-473,2004 Fig. 2. Performance of two incremental learning algorithms [7] D. Tax.: One-class classification. Ph D thesis, Delft University of From figure 2 we can see after each step of incremental Technology, htp://www.phtn.tudelft.nl/-davidt/thesispdf (2001) training, the variation of the predication accuracy on the test set is not various, which satisfy the requirement of algorithm [8] N A Syed, H Liu, K Sung. From incremental learning to model stability., and we can discovery the algorithm improvement is independent instance selection - a support vector machine gradually improved and algorithm and the algorithm own the approach, Technical Report, TRA9/99, NUS, 1999 ability of performance recoverability. So our incremental ablgoithmo perfopo ned inrthisoperabmeets the duriremand l o [9] L Yangguang, C Qi, T yongchuan et al. Incremental updating method for support vector machine, Apweb2004, LNCS 3007, incremental learnig. pp. 426-435, 2004. The experiment results show, our algorithm has the similar learning performance compared with the popular [10] S R Gunn. Support vector machines for classification and ISVM algorithm presented in [9]. Another discovery in our regression. Technical Report, Inage Speech and Intelligent experiment is with the gradually performing of our Systems Research Group, University of Southampton, 1997 incremental learning algorithm, the improvement of learning performance become less and less, and at last , the learning performance no longer improve. It indicates that we can estimate the needed number of samples required in problem description by using this character. 5. Conclusion In this paper we proposed an incremental learning algorithm based on support vector domain classifier (SVDC), and its key idea is to obtain the initial concept using standard SVDC, then using the updating technique presented in this paper, in fact which equals to solve a QP problem similar to that existing in standard SVDC algorithm solving. Experiments show that our algorithm is effective and promising. Others characters of this algorithm include: updating model has similar mathematics form compared with standard SVDC, and we can acquire the sparsity expression of its solutions, meanwhile using this algorithm can return last step without extra computation, furthermore, this algorithm can be used to estimate the needed number of samples required in problem description REFERENCES [1] C. Cortes, V. N. Vapnik.: Support vector networks, Mach. Learn. 20 (1995) pp. 273-297. [2] .V. N. Vapnik.: Statistical learning Theory, Wiley, New York, 1998. 809 2. Support Vector Domain Classifier with constrains , = =1, and 0 < a, < C. Where the 2.1 Support Vector Domain Description [7] inner product has been replaced with kernel function K(.,.), and K(.,.) is a definite kernel satisfying mercer Of a data setcontaiing N dataobj condition, for example a popular choice is the Gaussian Of a data set containing N data objects, enl (,)=ep-xz2/2),>0 f x, Z = 1, ... 9 10 [6] L. Baoqing. Distance-based selection of potential support vector Incremental Learning Step by kernel matrix. In International symposium on Neural (f) Networks 2004, LNCS 3173,pp. 468-473,2004 Fig. 2. Performance of two incremental learning algorithms [7] D. Tax.: One-class classification. Ph D thesis, Delft University of From figure 2 we can see after each step of incremental Technology, htp://www.phtn.tudelft.nl/-davidt/thesispdf (2001) training, the variation of the predication accuracy on the test set is not various, which satisfy the requirement of algorithm [8] N A Syed, H Liu, K Sung. From incremental learning to model stability., and we can discovery the algorithm improvement is independent instance selection - a support vector machine gradually improved and algorithm and the algorithm own the approach, Technical Report, TRA9/99, NUS, 1999 ability of performance recoverability. So our incremental ablgoithmo perfopo ned inrthisoperabmeets the duriremand l o [9] L Yangguang, C Qi, T yongchuan et al. Incremental updating method for support vector machine, Apweb2004, LNCS 3007, incremental learnig. pp. 426-435, 2004. The experiment results show, our algorithm has the similar learning performance compared with the popular [10] S R Gunn. Support vector machines for classification and ISVM algorithm presented in [9]. Another discovery in our regression. Technical Report, Inage Speech and Intelligent experiment is with the gradually performing of our Systems Research Group, University of Southampton, 1997 incremental learning algorithm, the improvement of learning performance become less and less, and at last , the learning performance no longer improve. It indicates that we can estimate the needed number of samples required in problem description by using this character. 5. Conclusion In this paper we proposed an incremental learning algorithm based on support vector domain classifier (SVDC), and its key idea is to obtain the initial concept using standard SVDC, then using the updating technique presented in this paper, in fact which equals to solve a QP problem similar to that existing in standard SVDC algorithm solving. Experiments show that our algorithm is effective and promising. Others characters of this algorithm include: updating model has similar mathematics form compared with standard SVDC, and we can acquire the sparsity expression of its solutions, meanwhile using this algorithm can return last step without extra computation, furthermore, this algorithm can be used to estimate the needed number of samples required in problem description REFERENCES [1] C. Cortes, V. N. Vapnik.: Support vector networks, Mach. Learn. 20 (1995) pp. 273-297. [2] .V. N. Vapnik.: Statistical learning Theory, Wiley, New York, 1998. 809 2. Support Vector Domain Classifier with constrains , = =1, and 0 < a, < C. Where the 2.1 Support Vector Domain Description [7] inner product has been replaced with kernel function K(.,.), and K(.,.) is a definite kernel satisfying mercer Of a data setcontaiing N dataobj condition, for example a popular choice is the Gaussian Of a data set containing N data objects, enl (,)=ep-xz2/2),>0 f x, Z = 1,...

... 10 4 300 400 500 600 700 800 900 1000 Number of Samples Number of Support Vectors 300 400 500 600 700 800 900 1000 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Number of Support Vectors Time (hours) 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5...

... specimen (an upper jaw and, later, a lower jaw) at FortTernaninsouthernKenya.Even The Truth About Human Origins -20- DifferencesinAnimalandHuman Sexuality . . . . 167 The Complexity of the Human ReproductiveSystem. ... and(3) hominin. An ex- planation is in order. Briefly stated, under the broad outlines of the Linnaeansys - tem,humanswouldbeclassifiedasfollows:Animalia(since man is consideredas an animal); Chordata(because ... rise totheancestorsofboth thehumanandtheape lines” (Leakey and Lewin, p. 56). And so the next creature in the evolutionary chart will be D. africanus, at about 20 million years. The Truth About Human Origins -18- Figure...

... the standardized resting boxes and the human landing catch for sampling malaria vectors and other mosquitoes in urban Dar es Salaam, Tanzania. Malar J 2009, 8:197. 38. Castro MC, Tsuruta A, Kanamori ... contributions PPC led the design and implemention of the study, data analysis and wrote the manuscript. AK and SD supported the design and implementation of the study. UF, KK and GFK designed and implemented ... Community-based surveillance of malaria vector larval habitats: a baseline study in urban Dar es Salaam, Tanzania. BMC Public Health 2006, 6:154. 24. Dunn FL: Human behavioural factors in mosquito vector control. Southeast...

... with femoral rotation, tibial rotation, and femoral rotation rel- ative to tibia. (Table 2) Through regression analysis, curve estimation has demon- strated that femoral rotation, tibial rotation, and ... predicted by femoral and tibial rotation, both exerted an R square of .54 (p < .01), in comparison to PTA-M, to which femoral rotation and tibial rotation exerted an R square of .35 and .14 respectively ... external tibial rotation, and increase in femoral rotation relative to tibia. (Figure 2, 3, 4) The measurements of femoral rotation, tibial rotation, and femoral rotation relative to tibia, and patella...