composite association combining innate (US) and acquired (CS) associations. We call models based on some variant of this hypothesis time-derivative models, examples of which are the models by Klopf (1988), Sutton and Barto

A new method, called the Fisher kernel method, for detecting remote protein homologies is introduced and shown to perform well in classifying protein domains by SCOP superfamily. The method is a variant of support vector machines using a new kernel function. The kernel function is derived from a

which models individual preferences as a convex combination of preference factors, and the two-sided clustering model, which simultaneously partitions persons and objects into clusters. We present EM algorithms for different variants of the aspect model and derive an approximate EM algorithm based

]. Recently, the first constant factor approximation algorithm for this problem was obtained by Shmoys, Tardos and Aardal [16]. We show that a simple greedy heuristic combined with the algorithm by Shmoys, Tardos and Aardal, can be used to obtain an approximation guarantee of 2.408. We discuss a few variants

glands. These findings prove that Mena could be a marker of premalignant epithelial lesions. In premalignant lesions, it could be helpful to define more accurately the risk for malignant transformation. In malignant tumors, correlation of expression of its splice variants could indicate metastatic

ABSTRACT-The extensive metastatic capacities in nude mice of var-iants of a human melanoma line, MeWo, were studied. BALB/c nude mice received subcutaneous implants of lung cubes impregnated in vitro with small numbers «1,000) of MeWo cells as a result of previous in vitro coincubation

This paper investigates a variant of boosting, L 2 Boost, which is constructed from a functional gradient descent algorithm with the L 2 -loss function. Based on an explicit stagewise re tting expression of L 2 Boost, the case of (symmetric) linear weak learners is studied in detail in both

The problem of optimally approximating a function with a linear expansion over a redundant dictionary of waveforms is NP-hard. The greedy matching pursuit algorithm and its orthogonalized variant produce sub-optimal function expansions by iteratively choosing dictionary waveforms that best match

and on the minimum distance of the resulting low-density parity-check (LDPC) codes are derived in terms of parameters of the graphs. The PEG construction attains essentially the same girth as Gallager's explicit construction for regular graphs, both of which meet or exceed the Erdos--Sachs bound. Asymptotic

derive and analyze two variants of the Expectation Maximization (EM) framework for estimating the mean and concentration parameters of this mixture. Numerical estimation of the concentration parameters is non-trivial in high dimensions since it involves functional inversion of ratios of Bessel functions