### Table 8: Repeatedly penalizing action 2 After penalizing action 2 six times, it still remains a high action probability. This indicates that the quadratic learning scheme has a similar tendency as the benevolent au- tomaton to stick to suboptimal actions. The linear reward-penalty scheme adapts quicker to environmental changes.

1991

"... In PAGE 14: ...he past, leading to its high action probability of 0.7. Now action 2 is repeatedly penalized. Table8 shows the changes in the probability vector, assuming A to be 0.5 and B to be 0.... ..."

### Table 2. QAP Coefficients Predicting Political Action Similarity from Network Embeddedness and Other Dyad Attributes: 57 U.S. Manufacturing Firms, 1980

2003

Cited by 21

### Table 1 lists in the flrst two columns the number of states of the model resulting from the learning pro- cess and the number of membership queries L* would need to learn the model. Roughly, the number of mem- bership queries is polynomial (between quadratic and cubic) in the number of states. The last two columns show the results of our optimized learning procedure, which exploits the speciflc proflle of our application scenario, like preflx-closedness of the language and in- dependence of certain actions. These optimizations, which lead to less than a quadratic number of queries per model state, are described in [?]. Particularly encouraging are the efiort reduction fac- tors of two orders of magnitude measured on the largest

2003

"... In PAGE 9: ... These optimizations, which lead to less than a quadratic number of queries per model state, are described in [?]. Particularly encouraging are the efiort reduction fac- tors of two orders of magnitude measured on the largest Table1 : Number of Membership Queries Scenario states membersh. optimized reduct.... ..."

Cited by 2

### Table 1. List of prioritized goals and recommended actions.

2004

"... In PAGE 15: ... Adult mortality is given as a quadratic equation where the probability of mortality in a given year increases with the age of the individual (see Table 1). Table1 . Age-specific mortality rates used for proboscis monkeys and the standard deviations in those rates due to environmental variation.... In PAGE 43: ... Table1 . Prioritized issues, goals and action plan for local community issues relating to proboscis monkey conservation.... ..."

### Table 2 Surface tension and latent heat for improved actions and the Wilson action. Results for the Wilson action are based on data from [27] using the non-perturbative -function calculated in [19]. Improved action results are taken from [11].

"... In PAGE 7: ... In fact, the results for N = 4 are in agreement with a quadratic extrap- olation of the Wilson data to the continuum limit. In Table2 we give results for I on the largest lattices considered. Clearly the surface tension ex- tracted from simulations with improved actions on lattices with temporal extent N = 4 are substan- tially smaller than corresponding results for the Wilson action.... ..."

### Table 2 Surface tension and latent heat for improved actions and the Wilson action. Results for the Wilson action are based on data from [27] using the non-perturbative -function calculated in [19]. Improved action results are taken from [11].

1997

"... In PAGE 7: ... In fact, the results for N = 4 are in agreement with a quadratic extrap- olation of the Wilson data to the continuum limit. In Table2 we give results for I on the largest lattices considered. Clearly the surface tension ex- tracted from simulations with improved actions on lattices with temporal extent N = 4 are substan- tially smaller than corresponding results for the Wilson action.... ..."

### TABLE 4.1 The generic limiting behavior (as x ! H) of the Hessian matrix Z(x) = (W;ij(x)), and of the correspond- ing tangent space T(x;p(x))Mu (S;0) R4. Z(x) ! Z(H) if x ! H from outside the wedge, i.e., along the manifold Mu (S;0). Similarly T(x;p(x))Mu (S;0) ! N(H). In Subcase A of the case lt; 1, Z(x) ! b Z(H) as x ! H from within the wedge; similarly T(x;p(x))Mu (S;0) ! b N(H). Each of N(H) and b N(H), if defined, is the linear span of a pair of eigenvectors of the linearized Hamiltonian flow T(H). In Subcase B and when gt; 1, the behavior of the action in the wedge is not quadratic near H, and no limiting Hessian matrix exists.

1997

Cited by 8

### Table 1. Prioritized issues, goals and action plan for local community issues relating to proboscis monkey conservation.

2004

"... In PAGE 15: ... Adult mortality is given as a quadratic equation where the probability of mortality in a given year increases with the age of the individual (see Table 1). Table1 . Age-specific mortality rates used for proboscis monkeys and the standard deviations in those rates due to environmental variation.... In PAGE 32: ...21 Table1 . List of prioritized goals and recommended actions.... ..."

### Table 1: Quadratic or Nonparametric?

2001

"... In PAGE 8: ... The squared L2 risks of the estimators are computed based on 100 replications. The numbers in the parentheses in Table1 are the corresponding standard errors. Quadratic regression works much better than the nonparametric alternatives for the rst two cases, but becomes much worse for the latter two due to lack of exibility.... ..."

Cited by 13

### Table Size for Quadratic Interpolation

1998

Cited by 1