It is not just in science that aims are problematic; this is the case in life too, either because different aims conflict, or because what we believe to be desirable and realizable lacks one or other of these features, or both. Above all, the aim of creating a wiser world is inherently and profoundly problematic. Quite generally, then, and not just in science, whenever we pursue a problematic aim we need to represent the aim as a hierarchy of aims, from the specific and problematic at the bottom of the hierarchy, to the general and unproblematic at the top. In this way we provide ourselves with a framework within which we may improve more or less specific and problematic aims and methods as we proceed, learning from success and failure in practice what it is that is both of most value and realizable. Such a hierarchical conception of rationality is the proper generalization of the hierarchical conception of science.
In Section 6 and Section 7, we present non-trivial applications of our Bayes risk lower bounds to twolearningproblems: the first one is a unsupervised learning problem, while the second one is a supervised learning problem. Section 6 studies smoothed analysis for learning mixtures of spherical Gaussians with uniform weights. Although learning mixtures of Gaussians is a computationally hard problem, it has been shown recently by Hsu and Kakade (2013) that under the assumptions that the Gaussian means are linearly indepen- dent, it can be learnt in polynomial time by a spectral method. We perform a smoothed analysis on a variant of the algorithm (Hsu and Kakade, 2013), showing that the linear independence assumption can be replaced by perturbing the true parameters by a small random noise. The method described in Section 6 achieves a better convergence rate than the original algorithm of Hsu and Kakade (2013). Furthermore, we apply the Bayes risk lower bound techniques to show that the algorithm’s convergence rate is unimprovable, even under smoothed analysis (i.e. when the true parameters are randomly perturbed). Section 6 highlights the usefulness of our techniques in proving lower bounds for smoothed analysis, which appears to be challenging using traditional techniques of the minimax theory.
We introduce a computationally feasible, “constructive” active learning method for binary classi- fication. The learning algorithm is initially formulated for separable classification problems, for a hyperspherical data space with constant data density, and for great spheres as classifiers. In or- der to reduce computational complexity the version space is restricted to spherical simplices and learning procedes by subdividing the edges of maximal length. We show that this procedure op- timally reduces a tight upper bound on the generalization error. The method is then extended to other separable classification problems using products of spheres as data spaces and isometries in- duced by charts of the sphere. An upper bound is provided for the probability of disagreement between classifiers (hence the generalization error) for non-constant data densities on the sphere. The emphasis of this work lies on providing mathematically exact performance estimates for active learning strategies.
If core knowledge is indeed expanding at a rate similar to that of non-core knowledge, then the strategy of solving the broad problem of knowledge expansion by defining a narrower core can only be a temporizing measure. On the other hand, if the quantum of core knowledge is deemed non-expansile – arbitrarily defined, for example, to repre- sent the amount of knowledge capable of being instilled in an average student by x teaching hours per week spread over y years – then any expansion of non-core knowledge will cause the core to shrink as a proportion of total knowledge. In 1984, for example, a list of two hundred drugs was hailed as a solution to information overload in the field of pharmacology ; but by 2000 the overload problem in this discipline was perceived to have deterio- rated despite both the embracement of PBL and relentless efforts to re-define a core curriculum .
algorithm can determine the correct class label. However, if no class has conditional probability greater than 0.5, then it is hard for the algorithm to determine the right class label. Second, the “one-vs-rest” approach has a very unbalanced sample size in their binary problems if the sample size for one class is much smaller than the union of all the other classes. Third, the “one-vs-one” approach has a much smaller samples size, which potentially increases the variance of the learned classifier. Fourth, these two de- composing strategies cannot capture the correlation between different classes, since they break a multicategory problem into multiple independent binary problems (Crammer and Singer, 2001). Therefore, a better way to inherit and extend the optimal property of binary SVM to the multicategory case is highly desired, which can classify multiple classes simultaneously.
Boosting is a learning method discovered by Schapire (1990). It proves computational equiva- lence between twolearning models: the model of distribution-free (strong) PAC-learning and that of distribution-free weak PAC-learning (the PAC-model was first introduced by Valiant, 1984; the strong and the weak cases were distinguished by Kearns and Valiant, 1994). This (theoretical) equiv- alence between the two models may be used to solve various problems in the domain of Learning Theory—a number of such problems are currently known whose “strong” solution was achieved by initially constructing a weak learner and then applying a boosting algorithm.
The way people view the world is determined wholly or partly by the structure of their native language. Following this way of reasoning, it seems obvious that in a situation when two people being users of different native languages meet, their view of the world, patterns of behavior and beliefs differ. Nowadays, in the era of globalization, more and more people move to another country to work or study and different cultures come into contact. Multiculturalism is an entrenched reality at university nowadays. Those who do not love it bear it, and those who accuse it are few. It defines the core of the moral mission of the contemporary university. Students, and also their tutors, seem to encounter problems concerning cultural clashes. Teaching and learning in a multicultural environment has, undoubtedly, advantages and disadvantages. As far as the negative aspect of learning and teaching in a multicultural environment is concerned, there are various problems encountered while two, or more different cultures come into contact. The problems are encountered not only by students, but by tutors and lecturers as well. As far as it concerns the students, and state that students enrolled in courses taught by professors coming from different ethnic or linguistic backgrounds experience discomfort, tension and conflict. It also applies to professors who experience such reservations towards foreigners and may encounter problems while marking them and trying to be honest. There are students who do not appreciate
Reinforcement Learning is learning from interactions with an environment, from the consequences of action, rather than from explicit teaching. It is essentially a simulation-based dynamic programming  and is primarily used to solve Markov Decision Problems. Reinforcement Learning algorithms are methods for solving problems involving sequences of decisions in which each decision affects what opportunities are available later, in which the effects are generally stochastic. RL algorithms  may estimate a value function and use it to construct better and better decision making policies over time. The two most important distinguishing features of Reinforcement Learning are trial and error search and Delayed reward. Model-free methods of RL do not need the transition probability matrices and hence avoid the curse of modeling. RL stores the value function in the form of Q-factors. An MDP has millions of states. It uses the function approximation methods, such as Neural Networks, regression and interpolation, which need only a small number of scalars to approximate Q- factors of these states and hence avoid the curse of dimensionality. A comparison of DP, RL and Heuristic algorithms is shown in Table-I.
The integration of technology in mathematics instruction is an important step in the 21st century learning style. At the primary level, some studies have explored how technology could help in mathematics learning. The purpose of this study is to determine the effect of using Logo on pupils’ learning of the properties of two- dimensional shapes. A total of 36 mixed ability Year 4 pupils from a primary school in Pahang, Malaysia participated in this study using the quasi experimental research design. The experimental group was taught using Logo while the control group was taught with the traditional method. The difference in achievement between the experimental group and control group was measured by pre-test and post-test. Results showed that the experimental group students performed better than the control group. Pupils’ perception toward using Logo was measured by using a questionnaire with close-ended items. The findings of this study indicated that using Logo improved pupils’ understanding of two- dimensional shapes. In addition, pupils have positive perception toward learning the properties of two-dimensional shapes using Logo.
Unlike the variable results for locations and years recorded for MSX samples, those of Dermo are more spatially and temporally consistent. While there has been no convincing evidence of oyster mortality due to Dermo prevalence, there are some irrefutable facts that would seem to implicate it as an agent in oyster stock decline in the Great Bay system. Upon review of the annual oyster survey record, 2008 to 2018, (Smith, B. and Sullivan, K., NHFG annual memos) it is clear the standing stock of adult oysters has trended downward. This has occurred even with historically high recruitment years in 2006 and 2007. This has been seen at both natural oyster beds where recreational harvest is ongoing (i.e. Nannie Is, Woodman Pt. and Adams Pt.) and at beds that are not harvested (i.e. Oyster River and Squamscott River).
Department (Smith, B., NHF&G annual memos), as well as information obtained via surveys of oyster harvesters, both abundance and harvest of oysters declined from 1995 to 1996 (NHF&G, 1997). It is highly likely that the presence of MSX and Dermo contributed significantly to these declines in the Great Bay oyster stock. More recent spatfalls (2006 to 2009 and 2014), however, are promising, with spat abundance at levels greater than those of the late 1990s through the early 2000s. This provided some optimism for the recovery of the stock. However, the most recent surveys of larger oysters show the stock once more slipping downward. It is imperative to maintain surveillance of these disease conditions, given that the presence (or absence) of such potentially damaging pathogens could indeed help explain the variability of oyster abundance in the future. The objective of this study is to monitor the presence of MSX and Dermo in Great Bay oysters .
The rote learning is the simplest machine learning method. The rote learning is the memory. That is, the new knowledge is stored, the supply and demand wants when retrieves transfers, but does not need to calculate and the inference. When the rote learning system operative to solve problems, the system remembers these questions and the solutions. We can regard the learning system execution part as some function abstractly, before calculating and outputting the function value (y1, y2,…, yp), this function obtains the independent variable input value (x1, x2,…, xn). The rote learning makes a simple memory storage in the memory ((x1, x2,…, xn), (y1, y2,…,yp)). When it needs f(x1, x2,…, xn), but the execution part on (y1, y2,…, yp) retrieves from the memory rather than recalculation. This kind of simple learning pattern is as follows: (x1,x2,…x n) f (y1,y2,… yp) store ((x1,x2,…x n), (y1,y2,…yp)).
C o m p u t a t i o n o f t h e c o o r d i n a t e s o f t h e r o t a t e d p o i n t i s m o s t c o n v e n i e n t l y c a r r i e d o u t b y a p p l y i n g t h e compound o f t h i s m a t r i x t o t h e c o o r d i n a t e s o f t h e compound p o i n t !Z = , .......... , • The f i r s t row o f t h e compound m a t r i x i s f o r m e d b y t r e a t i n g r ow s one a n d two o f A a s two p o i n t s i n R,_ a n d
During this same time, oyster beds in the Piscataqua and Salmon Falls Rivers (Maine) incurred similar, MSX-related mortality (Ken LaValley, University of New Hampshire Cooperative Extension, per. com.). The 1995 Great Bay Estuary MSX epizootic caused more than 80% mortality in the areas most affected (Barber et al., 1997). These highest mortalities were found in the Piscataqua and Salmon Falls Rivers. Other areas in the estuary did not appear to be as heavily infected. It is important to note that testing specifically for Dermo was not performed immediately after the reported oyster mortality in the fall of 1995. Dermo testing began in 1996, and has continued annually since then.
DOI: 10.4236/jss.2019.76006 88 Open Journal of Social Sciences 100,000 people were made slaves and only more than two hundred families were among the captives who were not taken as slaves” . Yu’s two sons and one daughter were both “killed one after another” in the war . Yu himself started to live in a strange land in the north. At the beginning of entering the north, Yu Xin was so angry about the war that he strongly condemned Yuwen Tai for “be- ing the son of a famous father, it was my fault that you were displaced between Jianghan” .
temperatures (Checkley et al., 1988), and proper substrates and vegetation (Nagelkerken et al., 2001; Keckeis et al., 1997; Lehtiniemi, 2005). Temperature has a great effect on egg and larval development (Pepin, 1991), as warmer waters can reduce stage duration by increasing growth rates (Otterlei et al.¸1999; Pepin, 1991; Pepin et al., 1997), thus decreasing the risk of mortality due to predation (Miller et al., 1988; Hare & Cowen 1997). The nursery habitats of freshwater species are often small and shallow, and because of this, they are vulnerable to temperature fluctuations (Houde, 1994) which can impact the survival of the developing larvae (Houde et al., 1988). Checkley et al., (1988) suggest that Atlantic menhaden (Brevoortia tyrannus) has evolved to spawn in winter storms near warm currents that the larvae utilize as shoreward transportation. However, many species’ spawning habits are not adapted to these conditions, and eggs and larvae can be carried by strong currents away from nursery areas. Deviation of physical processes away from those that the fish have been adapted to may explain variation in fish recruitment.
D α 0+ is the standard Riemann–Liouville derivative. Here our nonlinearity f may be singular at u = 0. Unlike the classical expression, we gave a new expression of the Green’s function and obtained some properties. As an application of Green’s function, we gave some mul- tiple positive solutions for singular positone and semipositone boundary value problems by means of the Leray–Schauder nonlinear alternative, a ﬁxed point theorem on cones. The results show that:
Orientation programs, demo lessons, guidance and counseling cell, internal quality assurance cell, annual planning, practice, learning by doing, learning by imitation, remedial teaching programme, annual social gathering, project method, field trips, interview programmes, technology based teaching activities, recording of the incidents and teaching, examinations, tutorial etc. all these programmes and strategies are used in these colleges.