Essays
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- Programming Bottom-Up - Страница 1
- Lisp for Web-Based Applications - Страница 3
- Beating the Averages - Страница 6
- Java's Cover - Страница 12
- Being Popular - Страница 14
- Five Questions about Language Design - Страница 24
- The Roots of Lisp - Страница 28
- The Other Road Ahead - Страница 29
- What Made Lisp Different - Страница 44
- Why Arc Isn't Especially Object-Oriented - Страница 45
- Taste for Makers - Страница 46
- What Languages Fix - Страница 52
- Succinctness is Power - Страница 53
- Revenge of the Nerds - Страница 57
- A Plan for Spam - Страница 65
- Design and Research - Страница 72
- Better Bayesian Filtering - Страница 76
- Why Nerds are Unpopular - Страница 82
- The Hundred-Year Language - Страница 90
- If Lisp is So Great - Страница 97
- Hackers and Painters - Страница 98
- Filters that Fight Back - Страница 105
- What You Can't Say - Страница 107
- The Word "Hacker" - Страница 114
- The Python Paradox - Страница 117
- Great Hackers - Страница 118
- The Age of the Essay - Страница 125
- What the Bubble Got Right - Страница 131
- Bradley's Ghost - Страница 136
- Made in USA - Страница 137
- What You'll Wish You'd Known - Страница 140
- How to Start a Startup - Страница 147
- A Unified Theory of VC Suckagepad - Страница 159
- Undergraduation - Страница 161
- Writing, Briefly - Страница 166
- Return of the Mac - Страница 167
- Why Smart People Have Bad Ideas - Страница 169
- The Submarine - Страница 173
- Hiring is Obsolete - Страница 177
- What Business Can Learn from Open Source - Страница 183
- After the Ladder - Страница 189
- Inequality and Risk - Страница 190
- What I Did this Summer - Страница 194
- Ideas for Startups - Страница 198
- The Venture Capital Squeeze - Страница 203
- How to Fund a Startup - Страница 205
- Web 2.0 - Страница 217
- How to Make Wealth - Страница 222
- Good and Bad Procrastination - Страница 233
- How to Do What You Love - Страница 236
- Are Software Patents Evil? - Страница 242
- The Hardest Lessons for Startups to Learn - Страница 248
- How to Be Silicon Valley - Страница 255
- Why Startups Condense in America - Страница 260
- The Power of the Marginal - Страница 267
- The Island Test - Страница 275
- Copy What You Like - Страница 276
- How to Present to Investors - Страница 278
- A Student's Guide to Startups - Страница 282
- The 18 Mistakes That Kill Startups - Страница 290
- Mind the Gap - Страница 297
- How Art Can Be Good - Страница 305
- Learning from Founders - Страница 310
- Is It Worth Being Wise? - Страница 311
- Why to Not Not Start a Startup - Страница 316
- Microsoft is Dead - Страница 324
- Two Kinds of Judgement - Страница 326
- The Hacker's Guide to Investors - Страница 327
- An Alternative Theory of Unions - Страница 336
- The Equity Equation - Страница 337
- Stuff - Страница 339
- Holding a Program in One's Head - Страница 341
- How Not to Die - Страница 344
- News from the Front - Страница 347
- How to Do Philosophy - Страница 350
- The Future of Web Startups - Страница 357
- Why to Move to a Startup Hub - Страница 362
- Six Principles for Making New Things - Страница 364
- Trolls - Страница 366
- A New Venture Animal - Страница 368
- You Weren't Meant to Have a Boss - Страница 371
It's hard to say what the overall false positive rate is, because we're up in the noise, statistically. Anyone who has worked on filters (at least, effective filters) will be aware of this problem. With some emails it's hard to say whether they're spam or not, and these are the ones you end up looking at when you get filters really tight. For example, so far the filter has caught two emails that were sent to my address because of a typo, and one sent to me in the belief that I was someone else. Arguably, these are neither my spam nor my nonspam mail.
Another false positive was from a vice president at Virtumundo. I wrote to them pretending to be a customer, and since the reply came back through Virtumundo's mail servers it had the most incriminating headers imaginable. Arguably this isn't a real false positive either, but a sort of Heisenberg uncertainty effect: I only got it because I was writing about spam filtering.
Not counting these, I've had a total of five false positives so far, out of about 7740 legitimate emails, a rate of .06%. The other two were a notice that something I bought was back-ordered, and a party reminder from Evite.
I don't think this number can be trusted, partly because the sample is so small, and partly because I think I can fix the filter not to catch some of these.
False positives seem to me a different kind of error from false negatives. Filtering rate is a measure of performance. False positives I consider more like bugs. I approach improving the filtering rate as optimization, and decreasing false positives as debugging.
So these five false positives are my bug list. For example, the mail from Egypt got nailed because the uppercase text made it look to the filter like a Nigerian spam. This really is kind of a bug. As with html, the email being all uppercase is really conceptually one feature, not one for each word. I need to handle case in a more sophisticated way.
So what to make of this .06%? Not much, I think. You could treat it as an upper bound, bearing in mind the small sample size. But at this stage it is more a measure of the bugs in my implementation than some intrinsic false positive rate of Bayesian filtering.
FutureWhat next? Filtering is an optimization problem, and the key to optimization is profiling. Don't try to guess where your code is slow, because you'll guess wrong. Look at where your code is slow, and fix that. In filtering, this translates to: look at the spams you miss, and figure out what you could have done to catch them.
For example, spammers are now working aggressively to evade filters, and one of the things they're doing is breaking up and misspelling words to prevent filters from recognizing them. But working on this is not my first priority, because I still have no trouble catching these spams [10].
There are two kinds of spams I currently do have trouble with. One is the type that pretends to be an email from a woman inviting you to go chat with her or see her profile on a dating site. These get through because they're the one type of sales pitch you can make without using sales talk. They use the same vocabulary as ordinary email.
The other kind of spams I have trouble filtering are those from companies in e.g. Bulgaria offering contract programming services. These get through because I'm a programmer too, and the spams are full of the same words as my real mail.
I'll probably focus on the personal ad type first. I think if I look closer I'll be able to find statistical differences between these and my real mail. The style of writing is certainly different, though it may take multiword filtering to catch that. Also, I notice they tend to repeat the url, and someone including a url in a legitimate mail wouldn't do that [11].
The outsourcing type are going to be hard to catch. Even if you sent a crawler to the site, you wouldn't find a smoking statistical gun. Maybe the only answer is a central list of domains advertised in spams [12]. But there can't be that many of this type of mail. If the only spams left were unsolicited offers of contract programming services from Bulgaria, we could all probably move on to working on something else.
Will statistical filtering actually get us to that point? I don't know. Right now, for me personally, spam is not a problem. But spammers haven't yet made a serious effort to spoof statistical filters. What will happen when they do?
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