The present status of 38 couples who had been treated at a clinic for sexual dysfunction 3 years previously was determined by a self-report assessment battery. The battery consisted of the Sexual Interaction Inventory, the Locke-Wallace Marriage Inventory, and the Sexual History Form completed at pretreatment, immediately posttreatment, 3 months after treatment, and at 3-year follow-up. An additional Follow-up Questionnaire was completed at the 3-year point only. At 3-year follow-up, analysis of data by diagnostic category indicated that sexual desire dysfunction for both men and women was particularly resistant to sustained behavioral change. Men with erectile difficulty reported significant improvement in their ability to maintain erections during intercourse but not in their ability to achieve erections prior to intercourse. Data from men with premature ejaculation revealed some immediate significant posttherapy gains, which, with the exception of length of foreplay, were not sustained at 3-year follow-up. For women with global inorgasmia, a significant increase in orgasmic response was reported. Data from women with situational orgasmic difficulties indicated some success in improving frequency of orgasm through masturbation and through genital caress; however, these changes did not reach statistical significance. Across all diagnostic categories, both men and women respondents reported increased satisfaction in their sexual relationship. Satisfaction in the marital relationship showed a more varied response pattern.
Prediction involves estimating the unknown value of an attribute of a system under study given the values of other measured attributes. In prediction (machine) learning the prediction rule is derived from data consisting of previously solved cases. Most methods for predictive learning were originated many years ago at the dawn of the computer age. Recently two new techniques have emerged that have revitalized the field. These are support vector machines and boosted decision trees. This paper provides an introduction to these two new methods tracing their respective ancestral roots to standard kernel methods and ordinary decision trees.
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